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<!DOCTYPE html> <html lang="en" class="no-js"> <head> <meta charset="UTF-8"> <meta http-equiv="X-UA-Compatible" content="IE=edge"> <meta name="applicable-device" content="pc,mobile"> <meta name="viewport" content="width=device-width, initial-scale=1"> <meta name="robots" content="max-image-preview:large"> <meta name="access" content="Yes"> <meta name="360-site-verification" content="1268d79b5e96aecf3ff2a7dac04ad990" /> <title>Estimating Total Rice Biomass and Crop Yield at Field Scale Using PlanetScope Imagery Through Hybrid Machine Learning Models | Earth Systems and Environment</title> <meta name="twitter:site" content="@SpringerLink"/> <meta name="twitter:card" content="summary_large_image"/> <meta name="twitter:image:alt" content="Content cover image"/> <meta name="twitter:title" content="Estimating Total Rice Biomass and Crop Yield at Field Scale Using PlanetScope Imagery Through Hybrid Machine Learning Models"/> <meta name="twitter:description" content="Earth Systems and Environment - Near real-time crop monitoring has been a challenging due to the lack of high-resolution remote sensing images suitable for agricultural applications. The..."/> <meta name="twitter:image" content="https://static-content.springer.com/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig1_HTML.png"/> <meta name="journal_id" content="41748"/> <meta name="dc.title" content="Estimating Total Rice Biomass and Crop Yield at Field Scale Using PlanetScope Imagery Through Hybrid Machine Learning Models"/> <meta name="dc.source" content="Earth Systems and Environment 2024 8:4"/> <meta name="dc.format" content="text/html"/> <meta name="dc.publisher" content="Springer"/> <meta name="dc.date" content="2024-10-18"/> <meta name="dc.type" content="OriginalPaper"/> <meta name="dc.language" content="En"/> <meta name="dc.copyright" content="2024 The Author(s)"/> <meta name="dc.rights" content="2024 The Author(s)"/> <meta name="dc.rightsAgent" content="journalpermissions@springernature.com"/> <meta name="dc.description" content="Near real-time crop monitoring has been a challenging due to the lack of high-resolution remote sensing images suitable for agricultural applications. The PlanetScope constellation, comprising approximately 130 Dove satellites, collects images of the entire Earth daily, with a resolution of 3.7&nbsp;m. The high-resolution images from the PlanetScope satellite, along with vegetation indices, geo-environmental data, and soil and crop parameters, were utilized and analysed using machine learning models to enhance the accuracy of predicting total biomass and rice crop yield at the field scale. The study area, covering nearly 214 sample rice plots, was located in the Tarekswar block of Hooghly, West Bengal, India. Alongside ten vegetation indices and three Principal Component Analysis (PCA) soil nutrient levels, approximately thirty-six factors were analyzed to predict rice total biomass and crop yield using ten machine learning (ML) models, namely Random forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), Bagging Tree (Treebag), Generalized Additive Models (gamSpline), Elastic Net (enet), Ordinary regression with LASSO penalty (rqlasso), Tree Models from Genetic Algorithm (evtree), Bayesian Regularized Neutral Networks (brnn), cubist models, and there hybrid of ensembles. Boruta and multi-collinearity analysis were also conducted for the selected factors to explore their influence levels. The study area exhibited robust rice yields ranging from 5 to 10 t/ha, accompanied by healthy biomass growth. Four ML models ─cubist, random forest, enet, and the hybrid model—showed promising predictions with R2 &gt; 0.88. Most models classified less than 20&nbsp;ha of the study area as falling into the “very-low suitable class”, showing the region’s suitability for rice cultivation due to its highly fertile alluvial soil. Boruta sensitive analysis revealed that nearly 24 individual factors significantly influenced the final crop yield including, organic carbon (OC), phosphorus (P), electrical conductivity (EC), mechanization level, and the majority of the vegetation indices. A critical analysis carried out through the Map query tool showed that five vegetation indices estimated via PlanetScope displayed strong correlations (exceeding 89%) in identifying areas with high to very high rice yields. The study can serve as a guideline for near-real-time crop monitoring in the near future, using high-resolution PlanetScope images."/> <meta name="prism.issn" content="2509-9434"/> <meta name="prism.publicationName" content="Earth Systems and Environment"/> <meta name="prism.publicationDate" content="2024-10-18"/> <meta name="prism.volume" content="8"/> <meta name="prism.number" content="4"/> <meta name="prism.section" content="OriginalPaper"/> <meta name="prism.startingPage" content="1713"/> <meta name="prism.endingPage" content="1731"/> <meta name="prism.copyright" content="2024 The Author(s)"/> <meta name="prism.rightsAgent" content="journalpermissions@springernature.com"/> <meta name="prism.url" content="https://link.springer.com/article/10.1007/s41748-024-00481-2"/> <meta name="prism.doi" content="doi:10.1007/s41748-024-00481-2"/> <meta name="citation_pdf_url" content="https://link.springer.com/content/pdf/10.1007/s41748-024-00481-2.pdf"/> <meta name="citation_fulltext_html_url" content="https://link.springer.com/article/10.1007/s41748-024-00481-2"/> <meta name="citation_journal_title" content="Earth Systems and Environment"/> <meta name="citation_journal_abbrev" content="Earth Syst Environ"/> <meta name="citation_publisher" content="Springer International Publishing"/> <meta name="citation_issn" content="2509-9434"/> <meta name="citation_title" content="Estimating Total Rice Biomass and Crop Yield at Field Scale Using PlanetScope Imagery Through Hybrid Machine Learning Models"/> <meta name="citation_volume" content="8"/> <meta name="citation_issue" content="4"/> <meta name="citation_publication_date" content="2024/12"/> <meta name="citation_online_date" content="2024/10/18"/> <meta name="citation_firstpage" content="1713"/> <meta name="citation_lastpage" content="1731"/> <meta name="citation_article_type" content="Original Article"/> <meta name="citation_fulltext_world_readable" content=""/> <meta name="citation_language" content="en"/> <meta name="dc.identifier" content="doi:10.1007/s41748-024-00481-2"/> <meta name="DOI" content="10.1007/s41748-024-00481-2"/> <meta name="size" content="244748"/> <meta name="citation_doi" content="10.1007/s41748-024-00481-2"/> <meta name="citation_springer_api_url" content="http://api.springer.com/xmldata/jats?q=doi:10.1007/s41748-024-00481-2&api_key="/> <meta name="description" content="Near real-time crop monitoring has been a challenging due to the lack of high-resolution remote sensing images suitable for agricultural applications. The "/> <meta name="dc.creator" content="Swain, Kishore Chandra"/> <meta name="dc.creator" content="Singha, Chiranjit"/> <meta name="dc.creator" content="Pradhan, Biswajeet"/> <meta name="dc.subject" content="Earth System Sciences"/> <meta name="dc.subject" content="Monitoring/Environmental Analysis"/> <meta name="dc.subject" content="Geography, general"/> <meta name="dc.subject" content="Environmental Science and Engineering"/> <meta name="dc.subject" content="Climate, general"/> <meta name="dc.subject" content="Climate Change/Climate Change Impacts"/> <meta name="citation_reference" content="Behzad S, Razavi M, Mahajeri M (1992) The effect of mineral nutrients (N.P.K.) on Saffron production. Acta Horticulture 306:426–430. https://doi.org/10.17660/ActaHortic.1992.306.56 "/> <meta name="citation_reference" content="citation_journal_title=Remote Sens; citation_title=Identifying the contributions of multi-source data for winter wheat yield prediction in China; citation_author=J Cao, Z Zhang, F Tao, L Zhang, Y Luo, J Han, Z Li; citation_volume=12; citation_issue=5; citation_publication_date=2020; citation_pages=750; citation_doi=10.3390/rs12050750; citation_id=CR1"/> <meta name="citation_reference" content="Cherlinka V (2024) Rice growth stages: roadmap of timely crop management. https://eos.com/crop-management-guide/rice-growth-stages/ . Accessed 10th Mar 2024"/> <meta name="citation_reference" content="citation_journal_title=Comput Electron Agric; citation_title=Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review; citation_author=A Chlingaryan, S Sukkarieh, B Whelan; citation_volume=151; citation_publication_date=2018; citation_pages=61-69; citation_doi=10.1016/j.compag.2018.05.012; citation_id=CR3"/> <meta name="citation_reference" content="Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, Meygret A, Spoto F, Sy O, Marchese F, Bargellini P (2012) Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens Environ 120:25-36. https://doi.org/10.1016/j.rse.2011.11.026 "/> <meta name="citation_reference" content="Durmmond S, Sudduth K, Buchleiter GW (2003) Soil electrical conductivity and topography related to yield for three vontrasting soil–crop systems. Agronomy J 953(3): 483-497."/> <meta name="citation_reference" content="FAOSTAT (2016) Food and Agriculture Organization of the United Nations (FAO). FAOSTAT Database. http://faostat.fao.org/site/291/default.aspx "/> <meta name="citation_reference" content="citation_journal_title=R Packag Version; citation_title=glmnet: Lasso and elastic-net regularized generalized linear models; citation_author=J Friedman, T Hastie, R Tibshirani; citation_volume=2009; citation_publication_date=2009; citation_pages=1; citation_id=CR4"/> <meta name="citation_reference" content="Ghosh BK (2010) Determinants of farm mechanisation in modern agriculture: a case study of burdwan districts of West Bengal. Int J Agril Res 5:1107–1115."/> <meta name="citation_reference" content="citation_journal_title=Livest Sci; citation_title=Accessing marker effects and heritability estimates from genome prediction by bayesian regularized neural networks; citation_author=LS Glória, CD Cruz, RAM Vieira, MDV Resende, PS Lopes, OHD Siqueira; citation_volume=191; citation_publication_date=2016; citation_pages=91-96; citation_doi=10.1016/j.livsci.2016.07.015; citation_id=CR5"/> <meta name="citation_reference" content="Gnyp ML, Bareth G, Li F, Lenz-Wiedemann VIS, Koppe W, Miao Y, Hennig SD, Liangliang J L, Laudien R, Xinping C X, Zhang F (2014) Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain. Int J Appl Earth Observ Geoinform 33:232–242."/> <meta name="citation_reference" content="citation_journal_title=J Stat Soft; citation_title=evtree: Evolutionary learning of globally optimal classification and regression trees in R; citation_author=T Grubinger, A Zeileis, KP Pfeiffer; citation_volume=61; citation_issue=1; citation_publication_date=2014; citation_pages=1-29; citation_doi=10.18637/jss.v061.i01; citation_id=CR6"/> <meta name="citation_reference" content="citation_journal_title=Agric For Meteorol; citation_title=Probabilistic forecasting of crop yields via quantile random forest and Epanechnikov Kernel function; citation_author=S Gyamerah, N Philip, I Dennis; citation_volume=280; citation_publication_date=2020; citation_pages=107808; citation_doi=10.1016/j.agrformet.2019.107808; citation_id=CR500"/> <meta name="citation_reference" content="citation_title=Generalized additive models; citation_inbook_title=Chapter 7 of Statistical Models in S; citation_publication_date=1992; citation_id=CR7; citation_author=TJ Hastie; citation_publisher=Wadsworth & Brooks/Cole"/> <meta name="citation_reference" content="citation_title=An Introduction to Statistical Learning: With Applications in R 1st ed; citation_publication_date=2013; citation_id=CR8; citation_author=G James; citation_author=W Daniela; citation_author=H Trevor; citation_author=T Robert; citation_publisher=Springer"/> <meta name="citation_reference" content="Jumpasut A, Fukuzato A, Zuleta I (2018) Using the moon as a calibration source for a fleet of satellites, conference on characterization and radiometric calibration for remote sensing, CALCON 2018,Utah State University, Logan, UT during 18–21 June 2018"/> <meta name="citation_reference" content="citation_journal_title=Int J Comput Appl; citation_title=Analysis of wheat production using Naïve Bayes classifier; citation_author=S Kaur, S Kalsi; citation_volume=178; citation_issue=14; citation_publication_date=2019; citation_pages=0975-8887; citation_id=CR10"/> <meta name="citation_reference" content="citation_journal_title=Front Plant Sci; citation_title=Crop yield prediction using deep neural networks; citation_author=S Khaki, L Wang; citation_volume=10; citation_publication_date=2019; citation_pages=621; citation_doi=10.3389/fpls.2019.00621; citation_id=CR11"/> <meta name="citation_reference" content="citation_journal_title=Comput Electron Agric; citation_title=Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield; citation_author=S Khanal, J Fulton, A Klopfenstein, N Douridas, S Shearer; citation_volume=153; citation_publication_date=2018; citation_pages=213-225; citation_doi=10.1016/j.compag.2018.07.016; citation_id=CR12"/> <meta name="citation_reference" content="citation_journal_title=ISPRS Int J Geo-Info; citation_title=A comparison between major artificial intelligence models for crop yield prediction: case study of the midwestern United States, 2006–2015; citation_author=N Kim, KJ Ha, NW Park, J Cho, S Hong, YW Lee; citation_volume=8; citation_issue=5; citation_publication_date=2019; citation_pages=240; citation_doi=10.3390/ijgi8050240; citation_id=CR13"/> <meta name="citation_reference" content="Kotchenova SY, Vermote EF, Matarrese R, Klemm FJ Jr (2006) Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: Path radiance. Appl Opt 45:6762–6774."/> <meta name="citation_reference" content="citation_journal_title=Land; citation_title=Estimating groundnut yield in smallholder agriculture systems using PlanetScope data; citation_author=D Kpienbaareh, K Mohammed, I Luginaah, J Wang, R Bezner Kerr, E Lupafya, L Dakishoni; citation_volume=11; citation_publication_date=2022; citation_pages=1752; citation_doi=10.3390/land11101752; citation_id=CR14"/> <meta name="citation_reference" content="citation_title=Applied Predictive Modeling 1st ed; citation_publication_date=2016; citation_id=CR15; citation_author=M Kuhn; citation_author=J Kjell; citation_publisher=Springer"/> <meta name="citation_reference" content="Piper CS (1942) Soil and plant analysis. The university of adelaide, Adelaide, p. 368"/> <meta name="citation_reference" content="citation_journal_title=Remote Sens; citation_title=Exploring the use of PlanetScope data for particulate matter air quality research; citation_author=J Roux, S Christopher, M Maskey; citation_volume=13; citation_publication_date=2021; citation_pages=2981; citation_doi=10.3390/rs13152981; citation_id=CR16"/> <meta name="citation_reference" content="citation_journal_title=Comput Electron Agric; citation_title=Normalization method for multi-sensor high spatial and temporal resolution satellite imagery with radiometric inconsistencies; citation_author=N Leach, NC Coops, N Obrknezev; citation_volume=164; citation_publication_date=2019; citation_doi=10.1016/j.compag.2019.104893; citation_id=CR17"/> <meta name="citation_reference" content="citation_journal_title=Agronomy; citation_title=In-season prediction of corn grain yield through PlanetScope and sentinel-2 images; citation_author=F Li, Y Miao, X Chen, Z Sun, K Stueve, F Yuan; citation_volume=12; citation_publication_date=2022; citation_pages=3176; citation_doi=10.3390/agronomy12123176; citation_id=CR18"/> <meta name="citation_reference" content="citation_journal_title=R News; citation_title=Classification and regression by randomforest; citation_author=A Liaw, M Wiener; citation_volume=2; citation_publication_date=2002; citation_pages=18-22; citation_id=CR19"/> <meta name="citation_reference" content="citation_journal_title=Agronomy; citation_title=Is high ph the key factor of alkali stress on plant growth and physiology? a case study with wheat (Triticum aestivum L.) seedlings; citation_author=D Liu, Y Ma, M Rui; citation_volume=12; citation_issue=8; citation_publication_date=2022; citation_pages=1820; citation_doi=10.3390/agronomy12081820; citation_id=CR20"/> <meta name="citation_reference" content="Martin CM (2009) Crop yield prediction using artificial neural networks and genetic algorithms. http://purl.galileo.usg.edu/uga_etd/martin_charles_m_200912_ms , http://hdl.handle.net/10724/26098 . Accessed 15 Mar 2023"/> <meta name="citation_reference" content="citation_journal_title=Artif Intell Life Sci; citation_title=Machine learning in agriculture domain: a state-of-art survey; citation_author=V Meshram, K Patil, V Meshram, D Hanchate, SD Ramkteke; citation_volume=1; citation_publication_date=2021; citation_id=CR22"/> <meta name="citation_reference" content="citation_journal_title=Compu Electron Agric; citation_title=Crop yield prediction with deep convolutional neural networks; citation_author=P Nevavuori, N Narra, T Lipping; citation_volume=163; citation_publication_date=2019; citation_doi=10.1016/j.compag.2019.104859; citation_id=CR23"/> <meta name="citation_reference" content="citation_journal_title=Acta Agric Scand Sect B Soil Plant Sci; citation_title=Predicting rainfed wheat quality and quantity by artificial neural network using terrain and soil characteristics; citation_author=M Norouzi, S Ayoubi, A Jalalian, H Khademi, AA Dehghani; citation_volume=60; citation_issue=4; citation_publication_date=2010; citation_pages=341-352; citation_id=CR24"/> <meta name="citation_reference" content="citation_journal_title=Sensors; citation_title=Evaluation of random forests (RF) for regional and local-scale wheat yield prediction in southeast Australia; citation_author=A Pang, MWL Chang, Y Chen; citation_volume=22; citation_issue=3; citation_publication_date=2022; citation_pages=717; citation_doi=10.3390/s22030717; citation_id=CR25"/> <meta name="citation_reference" content="citation_journal_title=Comput Elect Agricult; citation_title=Wheat yield prediction using machine learning and advanced sensing techniques; citation_author=XE Pantazi, D Moshou, T Alexandridis, RL Whetton, AM Mouazen; citation_volume=121; citation_publication_date=2016; citation_pages=57-65; citation_doi=10.1016/j.compag.2015.11.018; citation_id=CR26"/> <meta name="citation_reference" content="Quinlan JR (1993) Combining instance-based and model-based learning. In: proceedings of the 10th international conference on machine learning, Amherst, MA, USA, 27–29 June 1993, 236–243"/> <meta name="citation_reference" content="citation_title=Rice and potato yield prediction using artificial intelligence technique; citation_inbook_title=Intelligent of Things; citation_publication_date=2022; citation_pages=185-199; citation_id=CR28; citation_author=C Sangha; citation_author=KC Swain; citation_publisher=Springer"/> <meta name="citation_reference" content="citation_journal_title=Marine Pollut Bulle; citation_title=Potential for commercial PlanetScope satellites in oil response monitoring; citation_author=BA Schaeffer, P Whitman, R Conmy, W Salls, M Coffer, D Graybill, MC Lebrasse; citation_volume=183; citation_publication_date=2022; citation_doi=10.1016/j.marpolbul.2022.114077; citation_id=CR29"/> <meta name="citation_reference" content="Shakoor MT, Rahman K, Rayta SN, Chakrabarty A (2017) Agricultural production output prediction using supervised machine learning techniques. 2017 1st international conference on next generation computing applications (NextComp). https://doi.org/10.1109/nextcomp.2017.8016196 "/> <meta name="citation_reference" content="citation_journal_title=J Agric Engine; citation_title=Land suitability assessment for potato crop using analytic hierarchy process technique and geographic information system; citation_author=C Singha, KC Swain, BK Saren; citation_volume=56; citation_issue=3; citation_publication_date=2019; citation_pages=77-88; citation_id=CR31"/> <meta name="citation_reference" content="citation_journal_title=Agriculture; citation_title=Best crop rotation selection with GIS-AHP technique using soil nutrient variability; citation_author=C Singha, KC Swain, SK Swain; citation_volume=10; citation_issue=213; citation_publication_date=2020; citation_pages=1-18; citation_id=CR32"/> <meta name="citation_reference" content="citation_journal_title=India Remote Sens; citation_title=Spatial analysis of flood hazard zoning map using novel hybrid machine learning technique in Assam; citation_author=C Singha, KC Swain, M Meliho, HG Abdo, H Almohamad, M Al-Mutiry; citation_volume=14; citation_issue=24; citation_publication_date=2022; citation_pages=6229; citation_doi=10.3390/rs14246229; citation_id=CR33"/> <meta name="citation_reference" content="citation_journal_title=J App Remote Sens; citation_title=Apple yield prediction mapping using machine learning techniques through the google earth engine cloud in Kashmir valley, India; citation_author=C Singha, S Gulzar, KC Swain, D Pradhan; citation_volume=17; citation_issue=1; citation_publication_date=2023; citation_doi=10.1117/1.JRS.17.014505; citation_id=CR34"/> <meta name="citation_reference" content="citation_journal_title=Remote Sens; citation_title=Assessing within-field corn and soybean yield variability from worldView-3, planet, sentinel-2, and landsat 8 satellite imagery; citation_author=S Skakun, NI Kalecinski, MGL Brown, DM Johnson, EF Vermote, J Roger, B Franch; citation_volume=13; citation_publication_date=2021; citation_pages=872; citation_doi=10.3390/rs13050872; citation_id=CR35"/> <meta name="citation_reference" content="Soil Survey Staff (2000) Soil taxonomy, U.S. department, agriculture handbook, No. 436. U.S. government printing office, Washington, D.C. 754"/> <meta name="citation_reference" content="citation_journal_title=J R Stat Soc Ser B (Methodol); citation_title=Regression shrinkage and selection via the lasso; citation_author=R Tibshirani; citation_volume=58; citation_publication_date=1996; citation_pages=267-288; citation_doi=10.1111/j.2517-6161.1996.tb02080.x; citation_id=CR37"/> <meta name="citation_reference" content="citation_journal_title=Compu Electron Agric; citation_title=Crop yield prediction using machine learning: a systematic literature review; citation_author=T Klompenburg, A Kassahun, C Catal; citation_volume=177; citation_publication_date=2020; citation_doi=10.1016/j.compag.2020.105709; citation_id=CR38"/> <meta name="citation_reference" content="citation_journal_title=Int J Remote Sens; citation_title=Assessment of PlanetScope images for benthic habitat and seagrass species mapping in a complex optically shallow water environment; citation_author=P Wicaksono, W Lazuardi; citation_volume=39; citation_publication_date=2018; citation_pages=5739-5765; citation_doi=10.1080/01431161.2018.1506951; citation_id=CR39"/> <meta name="citation_reference" content="citation_journal_title=Int J Progress Sci Technol; citation_title=Landslide hazard zonation by using AHP (analytical hierarchy process) model in GIS (geographic information system) environment (case study: kordan watershed); citation_author=EA Yazdadi; citation_volume=2; citation_issue=1; citation_publication_date=2016; citation_pages=24-39; citation_doi=10.52155/ijpsat.v2.1.18; citation_id=CR40"/> <meta name="citation_reference" content="citation_journal_title=Energy; citation_title=Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination; citation_author=W Zhang, H Quan, D Srinivasan; citation_volume=160; citation_publication_date=2018; citation_pages=810-819; citation_doi=10.1016/j.energy.2018.07.019; citation_id=CR41"/> <meta name="citation_reference" content="citation_journal_title=BMC Bioinform; citation_title=A diabetes prediction model based on Boruta feature selection and ensemble learning; citation_author=H Zhou, Y Xin, S Li; citation_volume=24; citation_publication_date=2023; citation_pages=224; citation_doi=10.1186/s12859-023-05300-5; citation_id=CR42"/> <meta name="citation_author" content="Swain, Kishore Chandra"/> <meta name="citation_author_institution" content="Department of Agricultural Engineering, Institute of Agriculture, Visva-Bharati (A Central University), Bolpur, India"/> <meta name="citation_author" content="Singha, Chiranjit"/> <meta name="citation_author_institution" content="Department of Agricultural Engineering, Institute of Agriculture, Visva-Bharati (A Central University), Bolpur, India"/> <meta name="citation_author" content="Pradhan, Biswajeet"/> <meta name="citation_author_email" content="Biswajeet.pradhan@uts.edu.au"/> <meta name="citation_author_institution" content="Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia"/> <meta name="format-detection" content="telephone=no"/> <meta name="citation_cover_date" content="2024/12/01"/> <meta property="og:url" content="https://link.springer.com/article/10.1007/s41748-024-00481-2"/> <meta property="og:type" content="article"/> <meta property="og:site_name" content="SpringerLink"/> <meta property="og:title" content="Estimating Total Rice Biomass and Crop Yield at Field Scale Using PlanetScope Imagery Through Hybrid Machine Learning Models - Earth Systems and Environment"/> <meta property="og:description" content="Near real-time crop monitoring has been a challenging due to the lack of high-resolution remote sensing images suitable for agricultural applications. The PlanetScope constellation, comprising approximately 130 Dove satellites, collects images of the entire Earth daily, with a resolution of 3.7 m. The high-resolution images from the PlanetScope satellite, along with vegetation indices, geo-environmental data, and soil and crop parameters, were utilized and analysed using machine learning models to enhance the accuracy of predicting total biomass and rice crop yield at the field scale. The study area, covering nearly 214 sample rice plots, was located in the Tarekswar block of Hooghly, West Bengal, India. Alongside ten vegetation indices and three Principal Component Analysis (PCA) soil nutrient levels, approximately thirty-six factors were analyzed to predict rice total biomass and crop yield using ten machine learning (ML) models, namely Random forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), Bagging Tree (Treebag), Generalized Additive Models (gamSpline), Elastic Net (enet), Ordinary regression with LASSO penalty (rqlasso), Tree Models from Genetic Algorithm (evtree), Bayesian Regularized Neutral Networks (brnn), cubist models, and there hybrid of ensembles. Boruta and multi-collinearity analysis were also conducted for the selected factors to explore their influence levels. The study area exhibited robust rice yields ranging from 5 to 10 t/ha, accompanied by healthy biomass growth. Four ML models ─cubist, random forest, enet, and the hybrid model—showed promising predictions with R2 > 0.88. Most models classified less than 20 ha of the study area as falling into the “very-low suitable class”, showing the region’s suitability for rice cultivation due to its highly fertile alluvial soil. Boruta sensitive analysis revealed that nearly 24 individual factors significantly influenced the final crop yield including, organic carbon (OC), phosphorus (P), electrical conductivity (EC), mechanization level, and the majority of the vegetation indices. A critical analysis carried out through the Map query tool showed that five vegetation indices estimated via PlanetScope displayed strong correlations (exceeding 89%) in identifying areas with high to very high rice yields. The study can serve as a guideline for near-real-time crop monitoring in the near future, using high-resolution PlanetScope images."/> <meta property="og:image" content="https://static-content.springer.com/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig1_HTML.png"/> <meta name="format-detection" content="telephone=no"> <link rel="apple-touch-icon" sizes="180x180" href=/oscar-static/img/favicons/darwin/apple-touch-icon-92e819bf8a.png> <link rel="icon" type="image/png" sizes="192x192" href=/oscar-static/img/favicons/darwin/android-chrome-192x192-6f081ca7e5.png> <link rel="icon" type="image/png" sizes="32x32" href=/oscar-static/img/favicons/darwin/favicon-32x32-1435da3e82.png> <link rel="icon" type="image/png" sizes="16x16" href=/oscar-static/img/favicons/darwin/favicon-16x16-ed57f42bd2.png> <link rel="shortcut icon" data-test="shortcut-icon" href=/oscar-static/img/favicons/darwin/favicon-c6d59aafac.ico> <meta name="theme-color" content="#e6e6e6">   <link 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The PlanetScope constellation, comprising approximately 130 Dove satellites, collects images of the entire Earth daily, with a resolution of 3.7 m. The high-resolution images from the PlanetScope satellite, along with vegetation indices, geo-environmental data, and soil and crop parameters, were utilized and analysed using machine learning models to enhance the accuracy of predicting total biomass and rice crop yield at the field scale. The study area, covering nearly 214 sample rice plots, was located in the Tarekswar block of Hooghly, West Bengal, India. Alongside ten vegetation indices and three Principal Component Analysis (PCA) soil nutrient levels, approximately thirty-six factors were analyzed to predict rice total biomass and crop yield using ten machine learning (ML) models, namely Random forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), Bagging Tree (Treebag), Generalized Additive Models (gamSpline), Elastic Net (enet), Ordinary regression with LASSO penalty (rqlasso), Tree Models from Genetic Algorithm (evtree), Bayesian Regularized Neutral Networks (brnn), cubist models, and there hybrid of ensembles. Boruta and multi-collinearity analysis were also conducted for the selected factors to explore their influence levels. The study area exhibited robust rice yields ranging from 5 to 10 t/ha, accompanied by healthy biomass growth. Four ML models ─cubist, random forest, enet, and the hybrid model—showed promising predictions with R2 > 0.88. Most models classified less than 20 ha of the study area as falling into the “very-low suitable class”, showing the region’s suitability for rice cultivation due to its highly fertile alluvial soil. Boruta sensitive analysis revealed that nearly 24 individual factors significantly influenced the final crop yield including, organic carbon (OC), phosphorus (P), electrical conductivity (EC), mechanization level, and the majority of the vegetation indices. A critical analysis carried out through the Map query tool showed that five vegetation indices estimated via PlanetScope displayed strong correlations (exceeding 89%) in identifying areas with high to very high rice yields. The study can serve as a guideline for near-real-time crop monitoring in the near future, using high-resolution PlanetScope images.","datePublished":"2024-10-18T00:00:00Z","dateModified":"2024-10-18T00:00:00Z","pageStart":"1713","pageEnd":"1731","license":"http://creativecommons.org/licenses/by/4.0/","sameAs":"https://doi.org/10.1007/s41748-024-00481-2","keywords":["PlanetScope","Machine learning","Vegetation indices","Rice crop","Boruta analysis","Earth System Sciences","Monitoring/Environmental Analysis","Geography","general","Environmental Science and Engineering","Climate","Climate Change/Climate Change 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Pradhan","url":"http://orcid.org/0000-0001-9863-2054","affiliation":[{"name":"University of Technology Sydney","address":{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia","@type":"PostalAddress"},"@type":"Organization"}],"email":"Biswajeet.pradhan@uts.edu.au","@type":"Person"}],"isAccessibleForFree":true,"@type":"ScholarlyArticle"},"@context":"https://schema.org","@type":"WebPage"}</script> </head> <body class="" >  <noscript> <iframe src="https://www.googletagmanager.com/ns.html?id=GTM-MRVXSHQ" height="0" width="0" style="display:none;visibility:hidden"></iframe> </noscript>   <noscript data-test="gtm-body"> <iframe src="https://www.googletagmanager.com/ns.html?id=GTM-MRVXSHQ" height="0" width="0" style="display:none;visibility:hidden"></iframe> </noscript>  <div class="u-visually-hidden" aria-hidden="true" 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class="c-article-identifiers c-article-identifiers--cite-list"> <li class="c-article-identifiers__item"> <span data-test="journal-volume">Volume 8</span>, pages 1713–1731, (<span data-test="article-publication-year">2024</span>) </li> <li class="c-article-identifiers__item c-article-identifiers__item--cite"> <a href="#citeas" data-track="click" data-track-action="cite this article" data-track-category="article body" data-track-label="link">Cite this article</a> </li> </ul> <div class="app-article-masthead__buttons" data-test="download-article-link-wrapper" data-track-context="masthead"> <div class="c-pdf-container"> <div class="c-pdf-download u-clear-both u-mb-16"> <a href="/content/pdf/10.1007/s41748-024-00481-2.pdf" class="u-button u-button--full-width u-button--primary u-justify-content-space-between c-pdf-download__link" data-article-pdf="true" data-readcube-pdf-url="true" data-test="pdf-link" data-draft-ignore="true" data-track="content_download" data-track-type="article pdf 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Estimating Total Rice Biomass and Crop Yield at Field Scale Using PlanetScope Imagery Through Hybrid Machine Learning Models </div> <div data-test="inCoD" data-track-context="sticky banner"> <div class="c-pdf-container"> <div class="c-pdf-download u-clear-both u-mb-16"> <a href="/content/pdf/10.1007/s41748-024-00481-2.pdf" class="u-button u-button--full-width u-button--primary u-justify-content-space-between c-pdf-download__link" data-article-pdf="true" data-readcube-pdf-url="true" data-test="pdf-link" data-draft-ignore="true" data-track="content_download" data-track-type="article pdf download" data-track-action="download pdf" data-track-label="button" data-track-external download> <span class="c-pdf-download__text">Download PDF</span> <svg aria-hidden="true" focusable="false" width="16" height="16" class="u-icon"><use xlink:href="#icon-eds-i-download-medium"/></svg> </a> </div> </div> </div> </div> </div> <div class="c-article-header"> <header> <ul class="c-article-author-list c-article-author-list--short" data-test="authors-list" data-component-authors-activator="authors-list"><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Kishore_Chandra-Swain-Aff1" data-author-popup="auth-Kishore_Chandra-Swain-Aff1" data-author-search="Swain, Kishore Chandra">Kishore Chandra Swain</a><sup class="u-js-hide"><a href="#Aff1">1</a></sup>, </li><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Chiranjit-Singha-Aff1" data-author-popup="auth-Chiranjit-Singha-Aff1" data-author-search="Singha, Chiranjit">Chiranjit Singha</a><sup class="u-js-hide"><a href="#Aff1">1</a></sup> & </li><li class="c-article-author-list__item"><a data-test="author-name" data-track="click" data-track-action="open author" data-track-label="link" href="#auth-Biswajeet-Pradhan-Aff2" data-author-popup="auth-Biswajeet-Pradhan-Aff2" data-author-search="Pradhan, Biswajeet" data-corresp-id="c1">Biswajeet Pradhan<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-mail-medium"></use></svg></a><span class="u-js-hide"> <a class="js-orcid" href="http://orcid.org/0000-0001-9863-2054"><span class="u-visually-hidden">ORCID: </span>orcid.org/0000-0001-9863-2054</a></span><sup class="u-js-hide"><a href="#Aff2">2</a></sup> </li></ul> <div data-test="article-metrics"> <ul class="app-article-metrics-bar u-list-reset"> <li class="app-article-metrics-bar__item"> <p class="app-article-metrics-bar__count"><svg class="u-icon app-article-metrics-bar__icon" width="24" height="24" aria-hidden="true" focusable="false"> <use xlink:href="#icon-eds-i-accesses-medium"></use> </svg>547 <span class="app-article-metrics-bar__label">Accesses</span></p> </li> <li class="app-article-metrics-bar__item app-article-metrics-bar__item--metrics"> <p class="app-article-metrics-bar__details"><a href="/article/10.1007/s41748-024-00481-2/metrics" data-track="click" data-track-action="view metrics" data-track-label="link" rel="nofollow">Explore all metrics <svg class="u-icon app-article-metrics-bar__arrow-icon" width="24" height="24" aria-hidden="true" focusable="false"> <use xlink:href="#icon-eds-i-arrow-right-medium"></use> </svg></a></p> </li> </ul> </div> <div class="u-mt-32"> </div> </header> </div> <div data-article-body="true" data-track-component="article body" class="c-article-body"> <section aria-labelledby="Abs1" data-title="Abstract" lang="en"><div class="c-article-section" id="Abs1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Abs1">Abstract</h2><div class="c-article-section__content" id="Abs1-content"><p>Near real-time crop monitoring has been a challenging due to the lack of high-resolution remote sensing images suitable for agricultural applications. The PlanetScope constellation, comprising approximately 130 Dove satellites, collects images of the entire Earth daily, with a resolution of 3.7 m. The high-resolution images from the PlanetScope satellite, along with vegetation indices, geo-environmental data, and soil and crop parameters, were utilized and analysed using machine learning models to enhance the accuracy of predicting total biomass and rice crop yield at the field scale. The study area, covering nearly 214 sample rice plots, was located in the Tarekswar block of Hooghly, West Bengal, India. Alongside ten vegetation indices and three Principal Component Analysis (PCA) soil nutrient levels, approximately thirty-six factors were analyzed to predict rice total biomass and crop yield using ten machine learning (ML) models, namely Random forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), Bagging Tree (Treebag), Generalized Additive Models (gamSpline), Elastic Net (enet), Ordinary regression with LASSO penalty (rqlasso), Tree Models from Genetic Algorithm (evtree), Bayesian Regularized Neutral Networks <i>(</i>brnn), cubist models, and there hybrid of ensembles. Boruta and multi-collinearity analysis were also conducted for the selected factors to explore their influence levels. The study area exhibited robust rice yields ranging from 5 to 10 t/ha, accompanied by healthy biomass growth. Four ML models ─cubist, random forest, enet, and the hybrid model—showed promising predictions with R<sup>2</sup> > 0.88. Most models classified less than 20 ha of the study area as falling into the “very-low suitable class”, showing the region’s suitability for rice cultivation due to its highly fertile alluvial soil. Boruta sensitive analysis revealed that nearly 24 individual factors significantly influenced the final crop yield including, organic carbon (OC), phosphorus (P), electrical conductivity (EC), mechanization level, and the majority of the vegetation indices. A critical analysis carried out through the Map query tool showed that five vegetation indices estimated via PlanetScope displayed strong correlations (exceeding 89%) in identifying areas with high to very high rice yields. The study can serve as a guideline for near-real-time crop monitoring in the near future, using high-resolution PlanetScope images.</p></div></div></section> <div data-test="cobranding-download"> </div> <section aria-labelledby="inline-recommendations" data-title="Inline Recommendations" class="c-article-recommendations" data-track-component="inline-recommendations"> <h3 class="c-article-recommendations-title" id="inline-recommendations">Similar content being viewed by others</h3> <div class="c-article-recommendations-list"> <div class="c-article-recommendations-list__item"> <article class="c-article-recommendations-card" itemscope itemtype="http://schema.org/ScholarlyArticle"> <div class="c-article-recommendations-card__img"><img src="https://media.springernature.com/w92h120/springer-static/cover-hires/book/978-3-030-53187-4?as=webp" loading="lazy" alt=""></div> <div class="c-article-recommendations-card__main"> <h3 class="c-article-recommendations-card__heading" itemprop="name headline"> <a class="c-article-recommendations-card__link" itemprop="url" href="https://link.springer.com/10.1007/978-3-030-53187-4_46?fromPaywallRec=false" data-track="select_recommendations_1" data-track-context="inline recommendations" data-track-action="click recommendations inline - 1" data-track-label="10.1007/978-3-030-53187-4_46">Rice Yield Prediction Using On-Farm Data Sets and Machine Learning </a> </h3> <div class="c-article-meta-recommendations" data-test="recommendation-info"> <span class="c-article-meta-recommendations__item-type">Chapter</span> <span class="c-article-meta-recommendations__date">© 2020</span> </div> </div> </article> </div> <div class="c-article-recommendations-list__item"> <article class="c-article-recommendations-card" itemscope itemtype="http://schema.org/ScholarlyArticle"> <div class="c-article-recommendations-card__img"><img src="https://media.springernature.com/w215h120/springer-static/image/art%3A10.1007%2Fs41976-022-00072-7/MediaObjects/41976_2022_72_Figa_HTML.png" loading="lazy" alt=""></div> <div class="c-article-recommendations-card__main"> <h3 class="c-article-recommendations-card__heading" itemprop="name headline"> <a class="c-article-recommendations-card__link" itemprop="url" href="https://link.springer.com/10.1007/s41976-022-00072-7?fromPaywallRec=false" data-track="select_recommendations_2" data-track-context="inline recommendations" data-track-action="click recommendations inline - 2" data-track-label="10.1007/s41976-022-00072-7">Evaluation of Nonparametric Machine-Learning Algorithms for an Optimal Crop Classification Using Big Data Reduction Strategy </a> </h3> <div class="c-article-meta-recommendations" data-test="recommendation-info"> <span class="c-article-meta-recommendations__item-type">Article</span> <span class="c-article-meta-recommendations__date">17 June 2022</span> </div> </div> </article> </div> <div class="c-article-recommendations-list__item"> <article class="c-article-recommendations-card" itemscope itemtype="http://schema.org/ScholarlyArticle"> <div class="c-article-recommendations-card__img"><img src="https://media.springernature.com/w215h120/springer-static/image/art%3A10.1007%2Fs11119-022-09897-0/MediaObjects/11119_2022_9897_Fig1_HTML.png" loading="lazy" alt=""></div> <div class="c-article-recommendations-card__main"> <h3 class="c-article-recommendations-card__heading" itemprop="name headline"> <a class="c-article-recommendations-card__link" itemprop="url" href="https://link.springer.com/10.1007/s11119-022-09897-0?fromPaywallRec=false" data-track="select_recommendations_3" data-track-context="inline recommendations" data-track-action="click recommendations inline - 3" data-track-label="10.1007/s11119-022-09897-0">Statistical and machine learning methods for crop yield prediction in the context of precision agriculture </a> </h3> <div class="c-article-meta-recommendations" data-test="recommendation-info"> <span class="c-article-meta-recommendations__item-type">Article</span> <span class="c-article-meta-recommendations__date">30 March 2022</span> </div> </div> </article> </div> </div> </section> <script> window.dataLayer = window.dataLayer || []; window.dataLayer.push({ recommendations: { recommender: 'semantic', model: 'specter', policy_id: 'NA', timestamp: 1732692833, embedded_user: 'null' } }); </script> <div class="app-card-service" data-test="article-checklist-banner"> <div> <a class="app-card-service__link" data-track="click_presubmission_checklist" data-track-context="article page top of reading companion" data-track-category="pre-submission-checklist" data-track-action="clicked article page checklist banner test 2 old version" data-track-label="link" href="https://beta.springernature.com/pre-submission?journalId=41748" data-test="article-checklist-banner-link"> <span class="app-card-service__link-text">Use our pre-submission checklist</span> <svg class="app-card-service__link-icon" aria-hidden="true" focusable="false"><use xlink:href="#icon-eds-i-arrow-right-small"></use></svg> </a> <p class="app-card-service__description">Avoid common mistakes on your manuscript.</p> </div> <div class="app-card-service__icon-container"> <svg class="app-card-service__icon" aria-hidden="true" focusable="false"> <use xlink:href="#icon-eds-i-clipboard-check-medium"></use> </svg> </div> </div> <div class="main-content"> <section data-title="Introduction"><div class="c-article-section" id="Sec1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec1"><span class="c-article-section__title-number">1 </span>Introduction</h2><div class="c-article-section__content" id="Sec1-content"><p>Remote sensing has been successfully used in the crop yield estimation, crop scouting, and near-real-time crop status monitoring. Sentinel 2, IRS satellites, and even Landsat satellite images have been used in monitoring crops and detecting changes in crop status, including biotic crop stress. With the availability of higher spatial and temporal resolution images, the suitability, applicability, and adoption of remote sensing images have been significantly enhanced. According to research by Skakun et al. (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2021" title="Skakun S, Kalecinski NI, Brown MGL, Johnson DM, Vermote EF, Roger J, Franch B (2021) Assessing within-field corn and soybean yield variability from worldView-3, planet, sentinel-2, and landsat 8 satellite imagery. Remote Sens 13:872" href="/article/10.1007/s41748-024-00481-2#ref-CR35" id="ref-link-section-d77895435e441">2021</a>), the explained yield variability increased to 59%, 72%, and 86%, respectively, when finer resolution data of 30 m, 20 m, and 10 m were used.</p><p>Biomass growth is directly correlated with crop growth and ultimately crop yield. Healthy crop growth is typically indicative of a good crop yield, making it a crucial aspect of crop growth and yield estimation. Enhanced biomass growth often leads to improved photosynthesis when other influencing factors are present. Traditional biomass quantification methods typically involve destructive measures (Gnyp et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2014" title="Gnyp ML, Bareth G, Li F, Lenz-Wiedemann VIS, Koppe W, Miao Y, Hennig SD, Liangliang J L, Laudien R, Xinping C X, Zhang F (2014) Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain. Int J Appl Earth Observ Geoinform 33:232–242." href="/article/10.1007/s41748-024-00481-2#ref-CR301" id="ref-link-section-d77895435e447">2014</a>), requiring physical harvesting and measurement, which incur significant costs, labor, and time. Moreover, estimating biomass for large fields using destructive techniques may be impractical. Crop yield estimation has been carried out using optical remote sensing for some time. Moderate spatial resolution data from polar-orbiting satellites such as Landsat-8, Sentinel-2A, and 2B are effective in identifying various human activities (Drusch et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2012" title="Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, Meygret A, Spoto F, Sy O, Marchese F, Bargellini P (2012) Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens Environ 120:25-36. 
 https://doi.org/10.1016/j.rse.2011.11.026
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR300" id="ref-link-section-d77895435e450">2012</a>). However, medium-resolution imagery from satellites like Landsat often falls short in providing accurate estimations. Consequently, the final predictions may not be precise enough to support the implementation of preventive measures against factors limiting crop growth. This limitation is particularly pronounced in developing countries where small farm sizes make it challenging to implement site-specific management using medium-resolution satellite images.</p><p>Therefore, there is an immediate need for high resolution satellite imagery to facilitate the adoption of precision agriculture technology. High-resolution images enable site-specific management and variable-rate application of inputs. The PlanetScope constellation, consisting of approximately 130 Dove satellites, is capable of capturing images of nearly the entire Earth on a daily basis. The constellation comprises three set of sensor systems: the 3-band frame Dove-C, the 4-band frame Dove-R, and the 8-band frame of Coastal blue, Blue, Green, Green I, Yellow, Red, Red edge, and NIR spectrum under the SuperDove sensor systems (Jumpasut et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2018" title="Jumpasut A, Fukuzato A, Zuleta I (2018) Using the moon as a calibration source for a fleet of satellites, conference on characterization and radiometric calibration for remote sensing, CALCON 2018,Utah State University, Logan, UT during 18–21 June 2018" href="/article/10.1007/s41748-024-00481-2#ref-CR9" id="ref-link-section-d77895435e456">2018</a>). The ground sample resolution of Dove-C and Dove-R is between 3 and 4.1 m, while the SuperDove resolution is around 3.7 m (Supplementary Table 1). Cloud masking, atmospheric correction, geolocation, and radiance conversion to top-of-atmospheric reflectance are all included in the standard Level 3B PlanetScope swath image result. Utilising the 6S radiative transfer code, the data are atmospherically corrected to surface reflectance (Kotchenova et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2006" title="Kotchenova SY, Vermote EF, Matarrese R, Klemm FJ Jr (2006) Validation of a vector version of the 6S radiative
transfer code for atmospheric correction of satellite data. Part I: Path radiance. Appl Opt 45:6762–6774." href="/article/10.1007/s41748-024-00481-2#ref-CR302" id="ref-link-section-d77895435e459">2006</a>). PlanetScope data has proven to be very useful in various applications, including aquatic environments (Wicaksono and Lazuardi <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2018" title="Wicaksono P, Lazuardi W (2018) Assessment of PlanetScope images for benthic habitat and seagrass species mapping in a complex optically shallow water environment. Int J Remote Sens 39:5739–5765. 
 https://doi.org/10.1080/01431161.2018.1506951
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR39" id="ref-link-section-d77895435e462">2018</a>), forest fire detection (Leach et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Leach N, Coops NC, Obrknezev N (2019) Normalization method for multi-sensor high spatial and temporal resolution satellite imagery with radiometric inconsistencies. Comput Electron Agric 164:104893. 
 https://doi.org/10.1016/j.compag.2019.104893
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR17" id="ref-link-section-d77895435e465">2019</a>), air quality monitoring (le Roux et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2021" title="le Roux J, Christopher S, Maskey M (2021) Exploring the use of PlanetScope data for particulate matter air quality research. Remote Sens 13:2981. 
 https://doi.org/10.3390/rs13152981
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR16" id="ref-link-section-d77895435e468">2021</a>), and oil spill monitoring (Schaeffer et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Schaeffer BA, Whitman P, Conmy R, Salls W, Coffer M, Graybill D, Lebrasse MC (2022) Potential for commercial PlanetScope satellites in oil response monitoring. Marine Pollut Bulle 183:114077. 
 https://doi.org/10.1016/j.marpolbul.2022.114077
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR29" id="ref-link-section-d77895435e472">2022</a>). Li et al. (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Li F, Miao Y, Chen X, Sun Z, Stueve K, Yuan F (2022) In-season prediction of corn grain yield through PlanetScope and sentinel-2 images. Agronomy 12:3176. 
 https://doi.org/10.3390/agronomy12123176
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR18" id="ref-link-section-d77895435e475">2022</a>) used PlanetScope data to estimate corn grain yield, with regression-based analysis showing a variability of up to 72% in corn field. Kpienbaareh et al. (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Kpienbaareh D, Mohammed K, Luginaah I, Wang J, Bezner Kerr R, Lupafya E, Dakishoni L (2022) Estimating groundnut yield in smallholder agriculture systems using PlanetScope data. Land 11:1752. 
 https://doi.org/10.3390/land11101752
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR14" id="ref-link-section-d77895435e478">2022</a>) estimated groundnut field-based yield using PlanetScope based vegetation indices (VIs) through machine learning (ML) regression models (i.e. Rrandom forest (RF) and Multiple Linear Regression (MLR)). The RF model (R<sup>2</sup> = 0.96, RMSE = 0.29 kg/ha) outperformed the MLR model.</p><p>ML is a relatively new technology with the potential to support farmers by minimizing farming losses through providing sound recommendations and deep insights into crops status (Meshram et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2021" title="Meshram V, Patil K, Meshram V, Hanchate D, Ramkteke SD (2021) Machine learning in agriculture domain: a state-of-art survey. Artif Intell Life Sci 1:100010" href="/article/10.1007/s41748-024-00481-2#ref-CR22" id="ref-link-section-d77895435e486">2021</a>; van Klompenburg et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="van Klompenburg T, Kassahun A, Catal C (2020) Crop yield prediction using machine learning: a systematic literature review. Compu Electron Agric 177:105709. 
 https://doi.org/10.1016/j.compag.2020.105709
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR38" id="ref-link-section-d77895435e489">2020</a>; Nevavuori et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Nevavuori P, Narra N, Lipping T (2019) Crop yield prediction with deep convolutional neural networks. Compu Electron Agric 163:104859. 
 https://doi.org/10.1016/j.compag.2019.104859
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR23" id="ref-link-section-d77895435e492">2019</a>; Chlingaryan et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2018" title="Chlingaryan A, Sukkarieh S, Whelan B (2018) Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review. Comput Electron Agric 151:61–69. 
 https://doi.org/10.1016/j.compag.2018.05.012
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR3" id="ref-link-section-d77895435e495">2018</a>). It allows farmers to leverage vast amounts of data on crop and soil status, climate change, and environmental variables (such as temperature, precipitation, oxygen, and carbon dioxide level) to make informed decisions on their crops. Widely adopted ML algorithms in crop biomass and yield predictions include Decision Trees RF classification (Gyamerah et al., <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Gyamerah S, Philip N, Dennis I (2020) Probabilistic forecasting of crop yields via quantile random forest and
Epanechnikov Kernel function. Agric For Meteorol 280:107808. 
 https://doi.org/10.1016/j.agrformet.2019.107808
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR500" id="ref-link-section-d77895435e498">2020</a>); Support Vector Machines (SVMs) ; Light Gradient Boosting Machine (LightGBM) (Cao et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Cao J, Zhang Z, Tao F, Zhang L, Luo Y, Han J, Li Z (2020) Identifying the contributions of multi-source data for winter wheat yield prediction in China. Remote Sens 12(5):750. 
 https://doi.org/10.3390/rs12050750
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR1" id="ref-link-section-d77895435e502">2020</a>); Naïve Bayes (Kaur and Kalsi <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Kaur S, Kalsi S (2019) Analysis of wheat production using Naïve Bayes classifier. Int J Comput Appl 178(14):0975–8887" href="/article/10.1007/s41748-024-00481-2#ref-CR10" id="ref-link-section-d77895435e505">2019</a>); k-means clustering ( Shakoor et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2017" title="Shakoor MT, Rahman K, Rayta SN, Chakrabarty A (2017) Agricultural production output prediction using supervised machine learning techniques. 2017 1st international conference on next generation computing applications (NextComp). 
 https://doi.org/10.1109/nextcomp.2017.8016196
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR30" id="ref-link-section-d77895435e508">2017</a>); Supervised Kohonen Networks (SKNs) (Pantazi et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2016" title="Pantazi XE, Moshou D, Alexandridis T, Whetton RL, Mouazen AM (2016) Wheat yield prediction using machine learning and advanced sensing techniques. Comput Elect Agricult 121:57–65. 
 https://doi.org/10.1016/j.compag.2015.11.018
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR26" id="ref-link-section-d77895435e511">2016</a>); Cubist (Khanal et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2018" title="Khanal S, Fulton J, Klopfenstein A, Douridas N, Shearer S (2018) Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield. Comput Electron Agric 153:213–225. 
 https://doi.org/10.1016/j.compag.2018.07.016
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR12" id="ref-link-section-d77895435e514">2018</a>); eXtreme Gradient Boosting (XGBoost) (Zhang et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2018" title="Zhang W, Quan H, Srinivasan D (2018) Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination. Energy 160:810–819" href="/article/10.1007/s41748-024-00481-2#ref-CR41" id="ref-link-section-d77895435e517">2018</a>); Artificial Neural Networks (ANNs) (Kim et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Kim N, Ha KJ, Park NW, Cho J, Hong S, Lee YW (2019) A comparison between major artificial intelligence models for crop yield prediction: case study of the midwestern United States, 2006–2015. ISPRS Int J Geo-Info 8(5):240. 
 https://doi.org/10.3390/ijgi8050240
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR13" id="ref-link-section-d77895435e521">2019</a>); Genetic Algorithms (GAs) (Martin <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2009" title="Martin CM (2009) Crop yield prediction using artificial neural networks and genetic algorithms. 
 http://purl.galileo.usg.edu/uga_etd/martin_charles_m_200912_ms
 
 , 
 http://hdl.handle.net/10724/26098
 
 . Accessed 15 Mar 2023" href="/article/10.1007/s41748-024-00481-2#ref-CR21" id="ref-link-section-d77895435e524">2009</a>); and ensemble Deep Neural Networks (Khaki and Wang <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Khaki S, Wang L (2019) Crop yield prediction using deep neural networks. Front Plant Sci 10:621" href="/article/10.1007/s41748-024-00481-2#ref-CR11" id="ref-link-section-d77895435e527">2019</a>). These models have been successfully applied for prediction using remotely sensed information (Landsat, Sentinel, IRS data etc.) in extremely precise cultivation practices. Because crop growth and the inter- or intra-predictor variables interact so intricately, crop yield modelling is difficult. Target maize yields have been determined using ANN (Norouzi et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2010" title="Norouzi M, Ayoubi S, Jalalian A, Khademi H, Dehghani AA (2010) Predicting rainfed wheat quality and quantity by artificial neural network using terrain and soil characteristics. Acta Agric Scand Sect B Soil Plant Sci 60(4):341–352" href="/article/10.1007/s41748-024-00481-2#ref-CR24" id="ref-link-section-d77895435e530">2010</a>) using soil properties (Durmmond et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2003" title="Durmmond S, Sudduth K, Buchleiter GW (2003) Soil electrical conductivity and topography related to yield for three vontrasting soil–crop systems. Agronomy J 953(3): 483-497." href="/article/10.1007/s41748-024-00481-2#ref-CR304" id="ref-link-section-d77895435e533">2003</a>).</p><p>In recent years, the availability of PlanetScope-based vegetation indices, along with ML models, has been employed to improve crop production prediction accuracy. In the artificial intelligence domain, stacking ensemble-based hybrid ML models have been recommended for their superior performance compared to standalone models. Implementing such approaches, like predicting crop biomass and yield, can assist small-scale producers in planning and mitigating the effects associated with low crop yields. Factors such as soil nutrients, soil reflectance, geo-environmental conditions, socioeconomic factors, crop phenology, and climatic conditions can be leveraged to develop novel empirical approaches for near real-time crop growth monitoring. While some studies have demonstrated the use of Sentinel-2 data and Unmanned Aerial Vehicle (UAVs) in commercial farming systems to predict the yield of different crops; similar research with PlanetScope data in small-scale farms is not prevalent. This study offers a widespread analysis of the major factors influencing the development, implementation, and utilization of precision agriculture techniques in ensuring food security. Despite the limited research on the utilization of these tools in small-scale farms, this study remains significant in guiding the planning and implementation of food security strategies. It contributes to the literature on the utilization of geospatial technologies in planning and implementing community resilience and food security initiatives for small farms.</p><p>In this study, alongside actual field-level rice biomass and yield data, 36 factors were utilized through ten ML models and their hybridization to estimate the rice biomass and crop yield. The best ML models were recommended for future crop growth monitoring. The study also aimed to examine the predictive power of VIs generated from high-resolution photos for agricultural yield, as well as the suitability of PlanetScope data for tracking rice crops.</p></div></div></section><section data-title="Methodology"><div class="c-article-section" id="Sec2-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec2"><span class="c-article-section__title-number">2 </span>Methodology</h2><div class="c-article-section__content" id="Sec2-content"><h3 class="c-article__sub-heading" id="Sec3"><span class="c-article-section__title-number">2.1 </span>Study Area</h3><p>The study area is located in Tarakeswar block of the Hooghly district of West Bengal, India, within the Alluvial Deltatic Plain formed by the Ganga River in Southern Bengal. It spans an estimated land cover of 300 ha, with corner coordinates ranging from 2,528,500 to 2,530,600 N latitude and 604,500 to 606,500 E longitude in zone 45 of Universal Transverse Mercator (UTM) system under the WGS 1984 datum (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s41748-024-00481-2#Fig1">1</a>). The research region has an average elevation of 40 m above mean sea level on average. According to the Indian Metrological Department (IMD) (2017), the area experiences a tropical monsoon-style climate with 1500 mm of yearly precipitation on average. July’s average summer temperature is 290 °C, while January’s average winter temperature is 150 °C. Alluvial soils make up the majority of soil classifications, with sandy loam and loamy soils accounting for 32% and 48% of the total cultivated area, respectively (Singha et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Singha C, Swain KC, Saren BK (2019) Land suitability assessment for potato crop using analytic hierarchy process technique and geographic information system. J Agric Engine 56(3):77–88" href="/article/10.1007/s41748-024-00481-2#ref-CR31" id="ref-link-section-d77895435e558">2019</a>). Major crops cultivated include rice and jute in the summer, and potato and lentil in the winter season (November–April), with irrigation facilities (Singha et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2020" title="Singha C, Swain KC, Swain SK (2020) Best crop rotation selection with GIS-AHP technique using soil nutrient variability. Agriculture 10(213):1–18" href="/article/10.1007/s41748-024-00481-2#ref-CR32" id="ref-link-section-d77895435e561">2020</a>). According to the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2016" title="FAOSTAT (2016) Food and Agriculture Organization of the United Nations (FAO). FAOSTAT Database. 
 http://faostat.fao.org/site/291/default.aspx
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR310" id="ref-link-section-d77895435e564">2016</a>) rice is the principal summer crop in the study region, sometimes grown consecutively in the same field. <i>Boro paddy</i> (transplanted rice) is also widely practised in the study areas for better profit margin. The <i>Kana Nadi</i> drainage stream, extracted from the Hooghly river, along with a number of deep tube wells, supplies the irrigation water in the study area.</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-1" data-title="Fig. 1"><figure><figcaption><b id="Fig1" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 1</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/1" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig1_HTML.png?as=webp"><img aria-describedby="Fig1" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig1_HTML.png" alt="figure 1" loading="lazy" width="685" height="983"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-1-desc"><p>Location of the study area (Tarekshwar block, Hooghly district, West Bengal, India)</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/1" data-track-dest="link:Figure1 Full size image" aria-label="Full size image figure 1" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><h3 class="c-article__sub-heading" id="Sec4"><span class="c-article-section__title-number">2.2 </span>Rice Crop Biomass and Yield Data Collection</h3><p>Rice has been a major crop grown during early July to early December 2022 (the kharif season) in the region. A handheld GPS receiver (e-Trex 20, Garmin, KS, USA) was used to randomly select 214 farm plots for carrying out field surveys during the Kharif season in 2022. Generally, rice growth periods are divided into three primary phases: Reproductive, vegetative development, and ripening . However, there could be seven separate phases of the rice crop growth cycle, namely—transplanting, vegetative, booting, heading, flowering, and harvesting—are based on the field investigation (Cherlinka <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2024" title="Cherlinka V (2024) Rice growth stages: roadmap of timely crop management. 
 https://eos.com/crop-management-guide/rice-growth-stages/
 
 . Accessed 10th Mar 2024" href="/article/10.1007/s41748-024-00481-2#ref-CR2" id="ref-link-section-d77895435e599">2024</a>). Some field preparation images were taken to understand the initial condition of the farms before transplanting.</p><p>From October to November of 2022, three PlanetScope surface reflectance scene products (Level 3B) were obtained during the main rice growing season (vegetative, booting, and heading stage) (Supplementary Table 2). Four bands with a 3 m spatial resolution make up Planet Scope normalised analytic images: blue band (455–515 nm), green band (500–590 nm), red band (590–670 nm), and near infrared band (780–860 nm). All the image scenes were projected using the WGS84-UTM45 projection system for the vegetation indices (Vis) analysis. Using field data, the rice crop’s in-situ above-ground biomass (AGB) was calculated. During the peak growing stage, five sample points were selected from each plot using a 0.5 m × 0.5 m quadrat to gather the biomass. After the crop plant was sun-dried to a moisture content of 14% (w.b.), the biomass weight was determined. After being weighed, the biomass sample was converted to kilogrammes per hectare. When the agricultural plots were being harvested, statistics on crop yield was also gathered. Field photo observations were used to confirm the dates of each associated satellite scene.</p><h3 class="c-article__sub-heading" id="Sec5"><span class="c-article-section__title-number">2.3 </span>Selection of Parameters</h3><p>A comprehensive study was carried out to analyse the distribution of total rice biomass and crop yield using thirty-six parameters related to soil, crop practices, environment, and climate as well as remote sensing-based indices. The parameters were selected based on the high resolution images availability (Leach et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Leach N, Coops NC, Obrknezev N (2019) Normalization method for multi-sensor high spatial and temporal resolution satellite imagery with radiometric inconsistencies. Comput Electron Agric 164:104893. 
 https://doi.org/10.1016/j.compag.2019.104893
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR17" id="ref-link-section-d77895435e613">2019</a>) and prior study (Singha and Swain <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Singha C, Swain KC (2022) Rice and potato yield prediction using artificial intelligence technique. In: Pattnaik PK, Kumar R, Pal S (eds) Intelligent of Things, vol 3. Springer, Singapore, pp 185–199" href="/article/10.1007/s41748-024-00481-2#ref-CR28" id="ref-link-section-d77895435e616">2022</a>, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2019" title="Singha C, Swain KC, Saren BK (2019) Land suitability assessment for potato crop using analytic hierarchy process technique and geographic information system. J Agric Engine 56(3):77–88" href="/article/10.1007/s41748-024-00481-2#ref-CR31" id="ref-link-section-d77895435e619">2019</a>). They were classified under four major groups based on their relevance, suitability and correlations: soil nutrient parameters, geo-environmental parameters, agricultural practice parameters, and vegetation indices derived from PlanetScope imagery. These parameters were chosen to represent and monitor rice crop growth and yield in the study area.</p><h3 class="c-article__sub-heading" id="Sec6"><span class="c-article-section__title-number">2.4 </span>Data Analysis</h3><p>This research proposes a novel technique for developing intelligent models that are hybrid of ensembles ML and feature sensitivity techniques (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s41748-024-00481-2#Fig2">2</a>). The stages of the research work were as follows:</p><ul class="u-list-style-bullet"> <li> <p>Development of a rice crop monitoring model by collecting two types of datasets: field-based rice biomass and crop yield from 214 sample fields. These two data sets were used to predict the likelihood of rice biomass and crop yield as target variables.</p> </li> <li> <p>Incorporation of four major geospatial databases into rice biomass and crop yield predictors.</p> </li> <li> <p>Application of multi-collinearity evaluation (MCE) to assist regulating the delineation of independent parameters.</p> </li> <li> <p>Creation of rice biomass and yield models using the training dataset with hybrid models enabled by ensemble ML techniques.</p> </li> <li> <p>Evaluation of the effectiveness and performance of different ML models and hybridization using R<sup>2</sup>, RMSE, and MSE. The results were used to create the rice biomass and yield prediction maps.</p> </li> <li> <p>Identification of effective features for the model’s consistency according to Boruta analysis.</p> </li> </ul><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-2" data-title="Fig. 2"><figure><figcaption><b id="Fig2" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 2</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/2" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig2_HTML.png?as=webp"><img aria-describedby="Fig2" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig2_HTML.png" alt="figure 2" loading="lazy" width="685" height="210"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-2-desc"><p>Flowchart of methodology for rice biomass and yield estimation</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/2" data-track-dest="link:Figure2 Full size image" aria-label="Full size image figure 2" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec7"><span class="c-article-section__title-number">2.4.1 </span>Soil Nutrient Parameters</h4><p>In all, 214 plots from five different villages in the research region were randomly chosen for soil sampling. At the end of India’s Rabi season in May 2022, soil samples were taken down to a depth of 30 cm (Soil Survey Staff <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2000" title="Soil Survey Staff (2000) Soil taxonomy, U.S. department, agriculture handbook, No. 436. U.S. government printing office, Washington, D.C. 754" href="/article/10.1007/s41748-024-00481-2#ref-CR36" id="ref-link-section-d77895435e698">2000</a>). In the lab, the soil samples underwent a 2 mm sieved screen, drying, and cursing. Following conventional laboratory soil analysis protocols, the samples were then subjected to laboratory analysis for a number of selected parameters, including pH, soil texture, EC, organic C, accessible N, P, K, and Zn. Using the hydrometer method, the amount of sand, silt, and clay in the soil was estimated using the USDA approach (Piper <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 1942" title="Piper CS (1942) Soil and plant analysis. The university of adelaide, Adelaide, p. 368" href="/article/10.1007/s41748-024-00481-2#ref-CR312" id="ref-link-section-d77895435e701">1942</a>). By using the textural triangle diagram, the USDA method was used to establish the soil texture class. Parts per million, or PPM, was used to report the readings.</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec8"><span class="c-article-section__title-number">2.4.2 </span>Geo-environmental Parameters</h4><p>The geo-environmental parameters, such as maximum temperature, minimum temperature, precipitation, slope, and elevation, represent both climatic and topographic factors in the study area. Elevation and slope data were obtained from the ALOS PALSAR terrain-corrected (10 m spatial resolution) digital elevation model (URL: <a href="https://asf.alaska.edu/">https://asf.alaska.edu/</a>). The TerraClimate database was used to derive the climatic parameters (annual mean temperature and precipitation, 1958–2022) (URL: <a href="https://www.climatologylab.org/terraclimate.html">https://www.climatologylab.org/terraclimate.html</a>). Crop growth is directly affected by the volume and timing of precipitation, while photosynthesis is regulated by variations in temperature along with other inputs. Slope and elevation contribute to monitoring water flow direction and volume; however, the study area has nearly uniform levels of these parameters.</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec9"><span class="c-article-section__title-number">2.4.3 </span>Agricultural Practice Parameters</h4><p>Farmers generally practice similar cropping patterns with comparable levels of crop inputs. However, the modernization of farm activities has encouraged farmers to introduce new techniques on individual farmlands. In this study, agricultural practice parameters were divided into four sub-criteria: seed rate, mechanization level, irrigation level, and pesticide rate. A key element in the growth of successful and productive farming operations has been mechanisation in agriculture (Ghosh, <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2010" title="Ghosh BK (2010) Determinants of farm mechanisation in modern agriculture: a case study of burdwan districts of West Bengal. Int J Agril Res 5:1107–1115." href="/article/10.1007/s41748-024-00481-2#ref-CR311" id="ref-link-section-d77895435e734">2010</a>). It is possible to cultivate more land and save energy and resources (seed, fertiliser, and water) for sustainable agricultural output by increasing land and labour efficiency through the reduction of drudgery in farming operations . Experts usually determine the rates at which pesticides and seeds are applied. On the other hand, farmers may modify these rates in response to insect outbreaks and observations of seed quality.</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec10"><span class="c-article-section__title-number">2.4.4 </span>Local Variation Parameters</h4><p>These parameters included seven sub-criteria namely: farmer’s financial status, pest infestation, sources of irrigation, uses of farmyard manure (FYM), soil reflectance, and three principal component analyses (PC1, PC2, and PC3). Soil PCs were extracted from the VIs–NIR spectral curve (range 350-2500 nm) using ASD FieldSpec® data (Analytical Spectral Devices Inc., Boulder, CO, USA). Factors such as lack of training and rural infrastructure contribute to the development risk-prone conditions in these regions . However, these factors were not assessed primarily in the study. Behzad et al. (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 1992" title="Behzad S, Razavi M, Mahajeri M (1992) The effect of mineral nutrients (N.P.K.) on Saffron production. Acta Horticulture 306:426–430. 
 https://doi.org/10.17660/ActaHortic.1992.306.56
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR313" id="ref-link-section-d77895435e746">1992</a>) reported that cow manure is the greatest substitute for applying organic matter in the field to enhance the condition of the soil. Multi-criteria analysis, according to Antune et al. (2011), can be used to ascertain the state of farmers’ irrigation practices at the moment and to pinpoint the best irrigation options for every crop.</p><p>Local variation primarily arises from the level and type of interaction of farmers with their individual farms over time. There is a possibility of variation among farms as local conditions have a significant impact on farm practices. Nearly 95% of the variation in soil reflectance can be addressed through these three principal component analyses (PC1, PC2, PC3).</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec11"><span class="c-article-section__title-number">2.4.5 </span>Vegetation Indices</h4><p>Vegetation growth and variation are major indicators of crop health. The vegetation indices (VI) based on reflectance values with a 3-m resolution effectively represent true ground conditions. Leveraging the high-resolution images acquired through a 130 consortium of satellites will serve as a foundation for crop monitoring in the near future. Mean VIs were calculated through PlanetScope scenes during October to November 2022. Ten vegetation indices within the visible and infrared spectrum were utilized for model development under the vegetation indices criteria. The ten vegetation indices, such as Chlorophyll Index_Green (CI_Green), Normalized Difference Vegetation Index (NDVI), Green NDVI, Soil adjusted vegetation index (SAVI), Modified Triangular Vegetation Index (MTVI), Perpendicular vegetation index (PVI), Simple ratio (SR), Transferred soil adjusted vegetation index (TSAVI) and Visible Atmospherically Resistant Index (VARI) can be calculated as follows (Eqs. <a data-track="click" data-track-label="link" data-track-action="equation anchor" href="/article/10.1007/s41748-024-00481-2#Equ1">1</a> to <a data-track="click" data-track-label="link" data-track-action="equation anchor" href="/article/10.1007/s41748-024-00481-2#Equ10">10</a>).</p><div id="Equ1" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$${\text{CI}}\_{\text{Green}} = \frac{NIR}{{Green}} - 1,$$</span></div><div class="c-article-equation__number"> (1) </div></div><div id="Equ2" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$${\text{NDVI }} = \frac{NIR - Red}{{NIR + Red}},$$</span></div><div class="c-article-equation__number"> (2) </div></div><div id="Equ3" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$${\text{Green NDVI}} = \frac{NIR - Green}{{NIR + Green}},$$</span></div><div class="c-article-equation__number"> (3) </div></div><div id="Equ4" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$${\text{SAVI }} = \frac{NIR - Red}{{NIR + Red + L}}*\left( {{1}\, + \,{\text{L}}} \right),$$</span></div><div class="c-article-equation__number"> (4) </div></div><div id="Equ5" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$${\text{MSAVI }} = NIR + \frac{{1 - \sqrt {\left( {2*NIR + 1} \right)^{2} - 8\left( {NIR - R} \right)} }}{2},$$</span></div><div class="c-article-equation__number"> (5) </div></div><div id="Equ6" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$${\text{MTVI }} = \sqrt {\left( {\frac{c*NIR - Red}{{c*NIR + Red}}} \right)} ,$$</span></div><div class="c-article-equation__number"> (6) </div></div><div id="Equ7" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$${\text{PVI }} = \,\frac{{\left( {NIR - a*Red - b} \right)}}{{\sqrt {(1 + a^{2} )} }},$$</span></div><div class="c-article-equation__number"> (7) </div></div><div id="Equ8" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$${\text{SR }} = \frac{NIR}{{Red}},$$</span></div><div class="c-article-equation__number"> (8) </div></div><div id="Equ9" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$${\text{TSAVI }} = \,\frac{{s\left( {NIR - s*Red - a} \right)}}{{\left( {a*NIR + Red - a*s + X\left( {1 + s^{2} } \right)} \right)}},$$</span></div><div class="c-article-equation__number"> (9) </div></div><div id="Equ10" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$${\text{VARI }} = \,\frac{Green - Red}{{Green + Red - Blue}},$$</span></div><div class="c-article-equation__number"> (10) </div></div><p>Where,</p><p>CI_Green = Chlorophyll index_Green; NDVI = Normalized Difference Vegetation Index; Green NDVI = Green Normalized Difference Vegetation Index; SAVI = Soil adjusted vegetation index;</p><p>MTVI = Modified Triangular Vegetation Index; PVI = Perpendicular vegetation index;</p><p>SR = Simple ratio; TSAVI = Transferred soil adjusted vegetation index; VARI = Visible Atmospherically Resistant Index; L = Soil brightness correction factor; NIR = pixel values from the near-infrared band; Red = pixel values from the red band; Green = pixel values from the green band; Blue = pixel values from the blue band;s = the soil line slope; a = the soil line intercept; b = the weight factor; X = an adjustment factor that is set to minimize soil noise; c = weight factor.</p><h3 class="c-article__sub-heading" id="Sec12"><span class="c-article-section__title-number">2.5 </span>Multi-collinearity Evaluation</h3><p>In addition, the assessment of multi-collinearity evaluation assisted with the variance inflation factor (VIF) was carried out to address the issue of interrelatedness among variables that could affect the accuracy of rice biomass and yield prediction mapping (Eqs. <a data-track="click" data-track-label="link" data-track-action="equation anchor" href="/article/10.1007/s41748-024-00481-2#Equ11">11</a> & <a data-track="click" data-track-label="link" data-track-action="equation anchor" href="/article/10.1007/s41748-024-00481-2#Equ12">12</a>).</p><div id="Equ11" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$$Toleranc = R_{J}^{2}$$</span></div><div class="c-article-equation__number"> (11) </div></div><div id="Equ12" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$$VIF = \frac{1}{Tolerance}$$</span></div><div class="c-article-equation__number"> (12) </div></div><p>where,</p><p>VIP is Variance influence factor; <span class="mathjax-tex">$R_{J}^{2}$</span> is the explanator J regression’s coefficient of determination across all other explanatory variables.. The VIF’s threshold value is < 10 (Singha et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Singha C, Gulzar S, Swain KC, Pradhan D (2023) Apple yield prediction mapping using machine learning techniques through the google earth engine cloud in Kashmir valley, India. J App Remote Sens 17(1):014505. 
 https://doi.org/10.1117/1.JRS.17.014505
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR34" id="ref-link-section-d77895435e1753">2023</a>).</p><h3 class="c-article__sub-heading" id="Sec13"><span class="c-article-section__title-number">2.6 </span>Machine Learning Model Selection</h3><p>A machine learning algorithm is a procedure or technique that manipulates data to generate patterns and develop a model. Nine machine learning models were used to predict the total biomass and rice crop yield. Those models are random forest (RF), XGB, support vector machine (SVM), treebag, gamSpline, enet, rqlasso, evtree, brnn, cubist, and hybrid. Summaries of all ML models with tenfold validation iterations for biomass and yield are provided in Supplementary Tables 3 and 4.</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec14"><span class="c-article-section__title-number">2.6.1 </span>Random Forest (RF)</h4><p>It is an ensemble learning technique, which comprises of a number of decision trees (Liaw and Wiener <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2002" title="Liaw A, Wiener M (2002) Classification and regression by randomforest. R News 2:18–22" href="/article/10.1007/s41748-024-00481-2#ref-CR19" id="ref-link-section-d77895435e1771">2002</a>). The outcome from each decision tree is assembled, and the prediction with the majority of votes is presented as the final outcome. This model can be used for prediction and regression analysis.</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec15"><span class="c-article-section__title-number">2.6.2 </span>Extreme Gradient Boosting (XGB)</h4><p>This technique is based on the gradient boosting theory, which integrates the forecasts of multiple weak lean models to provide a highly praised training strategy. To carry out its functions, a set of parameter selections is needed to be supplied .</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec16"><span class="c-article-section__title-number">2.6.3 </span>Support Vector Machine (SVM)</h4><p>It finds the best decision boundaries in an N-dimensional space called as Hyperplane (James et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2013" title="James G, Daniela W, Trevor H, Robert T (2013) An Introduction to Statistical Learning: With Applications in R 1st ed. Springer, New York" href="/article/10.1007/s41748-024-00481-2#ref-CR8" id="ref-link-section-d77895435e1790">2013</a>). It can segregate data points into classes for getting decision boundaries. It identifies the extreme vector known as support vector.</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec17"><span class="c-article-section__title-number">2.6.4 </span>Bagged Tree (treebag)</h4><p>Instead of relying on a single decision tree, we rely on many decision trees, which allows to leverage the insight of many models (Kuhn and Kjell <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2016" title="Kuhn M, Kjell J (2016) Applied Predictive Modeling 1st ed. Springer, New York" href="/article/10.1007/s41748-024-00481-2#ref-CR15" id="ref-link-section-d77895435e1802">2016</a>).</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec18"><span class="c-article-section__title-number">2.6.5 </span>GamSpline</h4><p>Generalized additive models (GAMs) provide an effective method for capturing nonlinear correlations between a response variable and explanatory variables over a wide domains (Hastie <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 1992" title="Hastie TJ (1992) Generalized additive models. In: HastieChambers TJJM (ed) Chapter 7 of Statistical Models in S. Wadsworth & Brooks/Cole, Pacific Grove" href="/article/10.1007/s41748-024-00481-2#ref-CR7" id="ref-link-section-d77895435e1813">1992</a>). In the GamSpline model, the values of GAMs are used as foundation classifiers in an ensemble rather than traditional decision trees to increase the precision of predictions.</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec19"><span class="c-article-section__title-number">2.6.6 </span>Elasticnet (enet)</h4><p>Elasticnet (enet) uses shrinkage regression techniques to deal with multicollinearity evaluation. These techniques penalise the size of the regression coefficients to address multicollinearity. Lambda and alpha are two enet parameters that need to be optimised. In the leave-one-out cross-validation approach, the ideal lambda values for enet were chosen by reducing the average mean square error. In this study, ‘glmnet’ package was applied for enet analysis in R software (Friedman et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2009" title="Friedman J, Hastie T, Tibshirani R (2009) glmnet: Lasso and elastic-net regularized generalized linear models. R Packag Version 2009:1" href="/article/10.1007/s41748-024-00481-2#ref-CR4" id="ref-link-section-d77895435e1824">2009</a>).</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec20"><span class="c-article-section__title-number">2.6.7 </span>Ordinary Regression with LASSO Penalty (rqlasso)</h4><p>LASSO is a regularisation method that reduces the total number of predictors in a regression model while focusing on the most crucial ones (Tibshirani <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 1996" title="Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Methodol) 58:267–288" href="/article/10.1007/s41748-024-00481-2#ref-CR37" id="ref-link-section-d77895435e1835">1996</a>). The penalty factor in LASSO’s shrinkage estimator restricts the magnitude of the predicted coefficients and lowers prediction errors. It prefers a subset of characteristics without collinearity. LASSO simultaneously applies shrinkage and variable selection to improve prediction and model interpretation.</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec21"><span class="c-article-section__title-number">2.6.8 </span>Tree Models from Genetic Algorithms (evtree)</h4><p>Evtree model follows recursive partitioning techniques and is used to develop classification and regression tree algorithms (Grubinger et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2014" title="Grubinger T, Zeileis A, Pfeiffer KP (2014) evtree: Evolutionary learning of globally optimal classification and regression trees in R. J Stat Soft 61(1):1–29" href="/article/10.1007/s41748-024-00481-2#ref-CR6" id="ref-link-section-d77895435e1846">2014</a>). This model possesses an effective metaheuristic approach with a forward-looking manner. While it is regarded as an efficient technique, the results of these methods are only optimal when homogeneity is maximized at the next step.</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec22"><span class="c-article-section__title-number">2.6.9 </span>Bayesian Regularized Neutral Networks (brnn)</h4><p>According to Glória et al. (<a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2016" title="Glória LS, Cruz CD, Vieira RAM, de Resende MDV, Lopes PS, de Siqueira OHD et al (2016) Accessing marker effects and heritability estimates from genome prediction by bayesian regularized neural networks. Livest Sci 191:91–96. 
 https://doi.org/10.1016/j.livsci.2016.07.015
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR5" id="ref-link-section-d77895435e1857">2016</a>), brnn was attained by treating the feature vectors, or weights, as random parameters with a certain prior pattern in the feature space. The weights indicate the strength of interlinking within neurons. Broadly speaking, the structure of a BRN consists of three phases: (I) input data (independent parameters derived from an individual’s genomic data); (II) one hidden layer consisting of n neurons that connects the input and output sets; and (III) an output set consisting of a single neuron that produces the desired prediction scores as output.</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec23"><span class="c-article-section__title-number">2.6.10 </span>Cubist</h4><p>The Cubist model addresses residual dependencies by utilising local smoothing via k-nearest neighbours and committees for boosting linear models based on input datasets segmented using regression trees (Quinlan <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 1993" title="Quinlan JR (1993) Combining instance-based and model-based learning. In: proceedings of the 10th international conference on machine learning, Amherst, MA, USA, 27–29 June 1993, 236–243" href="/article/10.1007/s41748-024-00481-2#ref-CR27" id="ref-link-section-d77895435e1869">1993</a>). The major improvement in the Cubist method is that several training committees are included to balance case weight.</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec24"><span class="c-article-section__title-number">2.6.11 </span>Hybrid</h4><p>The results of the stacking ensemble suggested that it performed better than the standalone models (Singha et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Singha C, Swain KC, Meliho M, Abdo HG, Almohamad H, Al-Mutiry M (2022) Spatial analysis of flood hazard zoning map using novel hybrid machine learning technique in Assam. India Remote Sens 14(24):6229. 
 https://doi.org/10.3390/rs14246229
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR33" id="ref-link-section-d77895435e1880">2022</a>). The ten selected models used in the stacking with hybrid method performed well in terms of their performance. Therefore, this study applied the <i>get_stacking()</i> with majority of voting function in R for building the hybrid model.</p><h3 class="c-article__sub-heading" id="Sec25"><span class="c-article-section__title-number">2.7 </span>Performance Evaluation</h3><p>A few statistical calculations techniques were employed to evaluate the performance of the machine learning regression models. These calculations included the mean absolute error (MAE), root mean square error (RMSE) and determination coefficient (R<sup>2</sup>), as described in Eqs. <a data-track="click" data-track-label="link" data-track-action="equation anchor" href="/article/10.1007/s41748-024-00481-2#Equ13">13</a>–<a data-track="click" data-track-label="link" data-track-action="equation anchor" href="/article/10.1007/s41748-024-00481-2#Equ15">15</a>.</p><div id="Equ13" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$${\text{MAE}}\,{ = }\,\sum\nolimits_{1}^{n} {\frac{{|X_{i} \, - \,\hat{X}_{i} |}}{n},}$$</span></div><div class="c-article-equation__number"> (13) </div></div><div id="Equ14" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$${\text{RMSE}}\,{ = }\,\sqrt {\sum\nolimits_{1}^{n} {\frac{{\left( {X_{i} - \hat{X}_{i} } \right)^{2} }}{n}} } ,$$</span></div><div class="c-article-equation__number"> (14) </div></div><div id="Equ15" class="c-article-equation"><div class="c-article-equation__content"><span class="mathjax-tex">$$R^{2} = 1 - \frac{{\sum\nolimits_{i = 1}^{n} {\left( {X_{i} - \hat{X}_{i} } \right)^{2} } }}{{\sum\nolimits_{i = 1}^{n} {\left( {X_{i} - \overline{X}} \right)^{2} } }},$$</span></div><div class="c-article-equation__number"> (15) </div></div><p>Where <span class="mathjax-tex">$X_{i}$</span> is the observed training location of sample, <span class="mathjax-tex">$\hat{X}_{i}$</span> is the predicted sample, <span class="mathjax-tex">$\hat{X}_{i}$</span> is the actual mean value of sample score, i is the equal index, n is the total number of testing locations.</p><h4 class="c-article__sub-heading c-article__sub-heading--small" id="Sec26"><span class="c-article-section__title-number">2.7.1 </span>Boruta Sensitivity Analysis</h4><p>The selected parameters were subjected to statistical analysis to assess their sensitivity to variations in rice biomass and yield estimation. The Boruta method based on random forest-based was used to examine the multiple parameters contributing to the accuracy of the prediction (Zhou et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Zhou H, Xin Y, Li S (2023) A diabetes prediction model based on Boruta feature selection and ensemble learning. BMC Bioinform 24:224. 
 https://doi.org/10.1186/s12859-023-05300-5
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR42" id="ref-link-section-d77895435e2305">2023</a>).</p><h3 class="c-article__sub-heading" id="Sec27"><span class="c-article-section__title-number">2.8 </span>Vegetation Indices-Based Rice Yield Distribution Analysis</h3><p>The ten proposed vegetation indices, estimated from high-resolution PlanetScope imagery, were further analysed to evaluate yield distribution pattern of the study area. Since PlanetScope data is available on a nearly daily basis, the vegetation indices may be directly used to monitor the crop growth and ensure better crop yield. Areas showing higher vegetation indices values were compared with areas showing sound crop yield (under high and very-high crop yield). The Map Query tool of Arc GIS software environment was used to identify areas with higher vegetation index values as well as higher crop yield in the study area. Therefore, the most suitable vegetation indices may be recommended for future applications.</p></div></div></section><section data-title="Results"><div class="c-article-section" id="Sec28-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec28"><span class="c-article-section__title-number">3 </span>Results</h2><div class="c-article-section__content" id="Sec28-content"><h3 class="c-article__sub-heading" id="Sec29"><span class="c-article-section__title-number">3.1 </span>Distribution of Individual Parameters</h3><p>The distribution map of nutrients and crop inputs was carried out using the Arc GIS software environment for the entire study area covering 291 ha located in the Tarekeshwar block, Hooghly district, West Bengal, India. The entire study area was further classified into five classes based on the distribution of inputs, with each class having an equal range (Yazdadi <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2016" title="Yazdadi EA (2016) Landslide hazard zonation by using AHP (analytical hierarchy process) model in GIS (geographic information system) environment (case study: kordan watershed). Int J Progress Sci Technol 2(1):24–39. 
 https://doi.org/10.52155/ijpsat.v2.1.18
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR40" id="ref-link-section-d77895435e2330">2016</a>).</p><h3 class="c-article__sub-heading" id="Sec30"><span class="c-article-section__title-number">3.2 </span>Distribution of Geo-environmental Parameters</h3><p>During the research work, the temperature varied between 21 <sup>°</sup>C and 31.5 <sup>°</sup>C, which is nearly suitable for rice growth and vegetation. The annual average precipitation in the study area is around 140 cm. The slope of the study area is very-low, with values ranging from 0 to 7.5<sup>°</sup>. Similarly, as the study area is located near the sea, with the highest elevation reaching 25 m (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s41748-024-00481-2#Fig3">3</a>I).</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-3" data-title="Fig. 3"><figure><figcaption><b id="Fig3" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 3</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/3" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig3a_HTML.png?as=webp"><img aria-describedby="Fig3" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig3a_HTML.png" alt="figure 3" loading="lazy" width="685" height="922"></picture><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig3b_HTML.png?as=webp"><img aria-describedby="Fig3" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig3b_HTML.png" alt="figure 3" loading="lazy" width="685" height="942"></picture><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig3c_HTML.png?as=webp"><img aria-describedby="Fig3" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig3c_HTML.png" alt="figure 3" loading="lazy" width="685" height="742"></picture><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig3d_HTML.png?as=webp"><img aria-describedby="Fig3" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig3d_HTML.png" alt="figure 3" loading="lazy" width="685" height="916"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-3-desc"><p>Distribution of the individual parameters under primary criteria, <b>I</b> Geoenvironmental parameters, <b>II</b> Soil nutrient parameters, <b>III</b> Agricultural practice parameters, <b>IV</b> Local variation parameters, <b>V</b> Soil reflectance parameters, and <b>VI</b> Vegetation indices parameters</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/3" data-track-dest="link:Figure3 Full size image" aria-label="Full size image figure 3" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><h3 class="c-article__sub-heading" id="Sec31"><span class="c-article-section__title-number">3.3 </span>Distribution of Soil Nutrient Parameters</h3><p>Soil samples were collected from all the farmplots with a depth of 0-30 cm and were analysed in the laboratories following standard procedures. The pH values varied between 4 and 6, showing the presence of acidic soil in the study area. Situated in the Gangetic plain of alluvial soil, the study area is rich in clay content, making it suitable for agriculture. The level of nitrogen ranges between 12 and 65 ppm, and phosphorus ranges between 70 and 240 ppm.</p><h3 class="c-article__sub-heading" id="Sec32"><span class="c-article-section__title-number">3.4 </span>Agricultural Practice Parameters</h3><p>The mechanization level in the study area was found to be at a medium level. There is widespread use of tractors for field operations, and farmers were employing medium to higher levels of recommended pesticides. Additionally, all fields had irrigation facilities, which encouraged the cultivation of 2–3 crops per season.</p><h3 class="c-article__sub-heading" id="Sec33"><span class="c-article-section__title-number">3.5 </span>Local Variation Parameters</h3><p>The financial status mostly decided the level of mechanization and type of crop inputs, and vice versa. Interestingly, there was a significant application of farmyard manure (FYM) in the study area, leading to better crop yield.</p><h3 class="c-article__sub-heading" id="Sec34"><span class="c-article-section__title-number">3.6 </span>Soil Reflectance Parameters</h3><p>Soil reflectance was measured using the ASD FieldSpec® 3 portable soil spectrometer (Analytical Spectral Devices Inc., Boulder, CO, USA) in the laboratory. Through the principal component analysis, the level of influence at different wavelengths was measured and analysed. Nearly 95% of the variation was represented by the first 3 principal component models (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s41748-024-00481-2#Fig3">3</a>V).</p><h3 class="c-article__sub-heading" id="Sec35"><span class="c-article-section__title-number">3.7 </span>Vegetation Indices Parameters</h3><p>Nearly 10 vegetation indices parameters were included in the study to understand and capture all variations in crop growth through remote sensing with high resolution images. Widely used vegetation indices, such as the Normalized Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index, were also included in the study (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s41748-024-00481-2#Fig3">3</a>VI).</p><h3 class="c-article__sub-heading" id="Sec36"><span class="c-article-section__title-number">3.8 </span>Multi-collinearity Evaluation</h3><p>The analysis of the significance of independent variables’ Pearson correlation through multi-collinearity revealed mutual correlation between the influencing parameters and rice yield (Supplementary Fig. 1). A strong positive correlation was observed between rice yield and pH, OC, P, and the majority of vegetation index parameters including CI-Green, Green NDVI, MSAVI, NDVI, TSAVI, PVI, SR, etc. The positive relation with pH may raise eyebrows as the highest pH was found to be under 6.5 with the lowest value of 4.25. However, there is a negative correlation between the rice yield and pest affected area, available potassium, sand level, slope, Tmax along with seed rate, the third principal component (PC3), and financial status. As we understand, a lower seed rate leads to low crop yield; however, higher plant density was causing low overall crop yield. The phenomena of low rice yield with better financial status of the farmers could not be appreciably justified.</p><p>The multi-collinearity evaluation, though presenting regular phenomena of influence by the factors, still serve as an eye-opener to optimize rice yield in the study area. The minimum VIF score essential for model efficacy (< 10) (Singha et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2023" title="Singha C, Gulzar S, Swain KC, Pradhan D (2023) Apple yield prediction mapping using machine learning techniques through the google earth engine cloud in Kashmir valley, India. J App Remote Sens 17(1):014505. 
 https://doi.org/10.1117/1.JRS.17.014505
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR34" id="ref-link-section-d77895435e2451">2023</a>). Evaluation revealed that N (9.895) and pest affect (9.51) are the maximum VIF score of biomass and yield estimation, (Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/article/10.1007/s41748-024-00481-2#Tab1">1</a>).</p><div class="c-article-table" data-test="inline-table" data-container-section="table" id="table-1"><figure><figcaption class="c-article-table__figcaption"><b id="Tab1" data-test="table-caption">Table 1 Multi-collinearity assessment to measure the independent parameters linearity</b></figcaption><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="table-link" data-track="click" data-track-action="view table" data-track-label="button" rel="nofollow" href="/article/10.1007/s41748-024-00481-2/tables/1" aria-label="Full size table 1"><span>Full size table</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><h3 class="c-article__sub-heading" id="Sec37"><span class="c-article-section__title-number">3.9 </span>Total Biomass Estimation and Modelling Through Machine Learning</h3><p>Total biomass was estimated for all the rice plots during the research work. Rice biomass was collected from each plot, dried, and weighed. The weight of the sample biomass was expressed as ton/ha weight for the farm plot. The biomass was distributed in five classes ranging from 1.3 t/ha to 1.65 t/ha. Higher concentration of biomass was observed in the south-west region of the study area, with the lowest biomass in northeast region (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s41748-024-00481-2#Fig5">5</a>).</p><p>Ten machine learning models, along with a hybrid model, were used to predict total biomass production in the study area. The distribution maps were classified in five groups based on the weight of total biomass, namely very low suitable, low suitable, moderate suitable, highly suitable, and very highly suitable classes. The cubist ML model classified nearly 118.4 ha (40.1%) of the area as very highly suitable for producing rice total biomass. Similarly, all other models allocated the majority of the area as highly suitable, ranging between 30 and 50% of the total study area. Thus, only a marginal portion of the study area was classified under the very low suitable group for the production of rice biomass (Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/article/10.1007/s41748-024-00481-2#Tab2">2</a>; Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s41748-024-00481-2#Fig4">4</a>).</p><div class="c-article-table" data-test="inline-table" data-container-section="table" id="table-2"><figure><figcaption class="c-article-table__figcaption"><b id="Tab2" data-test="table-caption">Table 2 Areal coverage of total biomass (t/ha) and yield (in t/ha) distribution based on machine learning models</b></figcaption><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="table-link" data-track="click" data-track-action="view table" data-track-label="button" rel="nofollow" href="/article/10.1007/s41748-024-00481-2/tables/2" aria-label="Full size table 2"><span>Full size table</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-4" data-title="Fig. 4"><figure><figcaption><b id="Fig4" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 4</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/4" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig4_HTML.png?as=webp"><img aria-describedby="Fig4" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig4_HTML.png" alt="figure 4" loading="lazy" width="685" height="1242"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-4-desc"><p>Total Biomass prediction through machine learning models, <b>a</b> RF, <b>b</b> gamSpline, <b>c</b> enet, <b>d</b> rqlasso, <b>e</b> evtree, <b>f</b> brnn, <b>g</b> treebag, <b>h</b> xgbTree, <b>i</b> svmLinear, <b>j</b> cubist, and <b>k</b> hybrid</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/4" data-track-dest="link:Figure4 Full size image" aria-label="Full size image figure 4" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><p>The validation of total biomass models was carried out by following 70%:30% proportion data for training and testing, respectively. Most of the models performed well in predicting total biomass in the study area for validation datasets. Based on the coefficient of determination (R<sup>2</sup>) values, the random forest, cubist, and hybrid models showed more promising results (about > 0.96) compared to other models (Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/article/10.1007/s41748-024-00481-2#Tab3">3</a>). Besides the evtree ML model, all models showed higher biomass concentration in the southwest region of the study area. The summery statistics of the ML models for testing data sets of the biomass prediction performance results were summarized (Supplementary Table 5).</p><div class="c-article-table" data-test="inline-table" data-container-section="table" id="table-3"><figure><figcaption class="c-article-table__figcaption"><b id="Tab3" data-test="table-caption">Table 3 Performance of machine learning models in estimating total biomass and yield</b></figcaption><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="table-link" data-track="click" data-track-action="view table" data-track-label="button" rel="nofollow" href="/article/10.1007/s41748-024-00481-2/tables/3" aria-label="Full size table 3"><span>Full size table</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><h3 class="c-article__sub-heading" id="Sec38"><span class="c-article-section__title-number">3.10 </span>Rice Yield Estimation and Prediction</h3><p>Rice has been a major food staple in West Bengal, with an annual production of 16.6 million tons. Rice yield information from each plot was collected after the crop was harvested. The yield values were expressed in t/ha for each plot. Similar to the distribution of biomass, the southwest region of the study area had higher rice yield, while the northeast region had lower crop yield. The rice yield in the study area ranged between 5 t/ha and 10 t/ha (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s41748-024-00481-2#Fig5">5</a>).</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-5" data-title="Fig. 5"><figure><figcaption><b id="Fig5" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 5</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/5" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig5_HTML.png?as=webp"><img aria-describedby="Fig5" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig5_HTML.png" alt="figure 5" loading="lazy" width="685" height="340"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-5-desc"><p>Field based <b>a</b> Biomass distribution map (t/ha), and <b>b</b> Rice yield distribution map for the study area</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/5" data-track-dest="link:Figure5 Full size image" aria-label="Full size image figure 5" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><p>Rice yield prediction was carried out based on the distribution of six primary influencing criteria: geo-environmental parameters, static soil parameters, available soil nutrients, parameters, local variation parameters, and vegetation indices parameters, and total biomass level, including 36 individual factors. The vegetation indices were calculated from PlanetScope satellite data containing eight spectral bands: such as red edge, red, green, green I, yellow, blue, coastal blue and near infra-red. The random forest model predicted nearly 187.1 ha area as very highly suitable class, while a negligible area (0.1 ha) was classified under the not suitable class (Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/article/10.1007/s41748-024-00481-2#Tab2">2</a>; Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s41748-024-00481-2#Fig6">6</a>). Similarly, the treebag and evtree models allocated the majority of the study area to the very highly suitable class, with areas of 184.6 ha and 192 ha, respectively. However, the SVM, GamSpline, rglasso, brnn, and cubist models assigned the majority of the study area to the highly suitable class, with areas of 174.9 ha, 172.5 ha, 177 ha and 183.8 ha respectively. Most of the models classified < 20 ha of the study area as very low suitable class, showing the suitability of rice crop in the region with very fertile alluvial soil (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s41748-024-00481-2#Fig6">6</a>).</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-6" data-title="Fig. 6"><figure><figcaption><b id="Fig6" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 6</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/6" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig6_HTML.png?as=webp"><img aria-describedby="Fig6" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig6_HTML.png" alt="figure 6" loading="lazy" width="685" height="1160"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-6-desc"><p>Rice yield prediction through machine learning models: <b>a</b> RF, <b>b</b> gamSpline, <b>c</b> enet, <b>d</b> rqlasso, <b>e</b> evtree, <b>f</b> brnn, <b>g</b> treebag, <b>h</b> xgbTree, <b>i</b> svmLinear, <b>j</b> cubist, and <b>k</b> hybrid</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/6" data-track-dest="link:Figure6 Full size image" aria-label="Full size image figure 6" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><p>Most of the models comfortably predicted rice yield based on the influencing factors. Four ML models namely, hybrid, cubist, random forest, and enet showed promising predictions with R<sup>2</sup> > 0.88 with validation datasets. These results showed the suitability of ML models for predicting rice yield in future (Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/article/10.1007/s41748-024-00481-2#Tab3">3</a>). The summary statistics of the ML models for testing data sets of the yield prediction performance results were summarized (Supplementary Table 6).</p><h3 class="c-article__sub-heading" id="Sec39"><span class="c-article-section__title-number">3.11 </span>Boruta Sensitivity Analysis of Rice Biomass Factors</h3><p>Boruta analysis used to identify the effectiveness of individual factors for the rice biomass prediction. Around twenty-two individual factors were found to have a significant level of influence on total biomass prediction, including Cl_Green, clay, EC, financial status, and FYM etc. (Supplementary Table 7). On the other hand, 13 individual factors including elevation, slope, Z, K, level along with soil reflectance level showed low correlation (Figs. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s41748-024-00481-2#Fig7">7</a>a). The nitrogen level showed lower variation, as almost all farmers applies the fertilizer as much higher rates than the recommended level. Similarly, acidic pH has a higher level of influence, with higher values approaching neutrality.</p><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-7" data-title="Fig. 7"><figure><figcaption><b id="Fig7" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 7</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/7" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig7_HTML.png?as=webp"><img aria-describedby="Fig7" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig7_HTML.png" alt="figure 7" loading="lazy" width="685" height="599"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-7-desc"><p>Boruta parameters sensitivity analysis: <b>a</b> Biomass prediction, and <b>b</b> Rice yield prediction</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/7" data-track-dest="link:Figure7 Full size image" aria-label="Full size image figure 7" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><h3 class="c-article__sub-heading" id="Sec40"><span class="c-article-section__title-number">3.12 </span>Boruta Sensitivity Analysis of Rice Yield Factors</h3><p>The Boruta analysis of rice yield for individual factors was carried out to identify the level of influence of each factor on the final crop yield (Supplementary Table 8). Nearly 24 individual factors showed a significant level of influence on the final crop yield, including OC, P, EC, mechanization level, along with the majority of the vegetation indices. Similarly, variation in nearly 12 factors showed little impact on rice yield, namely potassium, elevation, and slope among others (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s41748-024-00481-2#Fig7">7</a>b). The importance of factors such as variation in available nitrogen and pesticide rate, which are obvious in affecting the final crop yield, was rejected, as most of the farmers applied higher-than-recommended levels of such inputs. The higher rate of application of inputs levels leaves no scope to study their level of influence on crop yield. The impact of these parameters may be estimated through randomized block design-based experiment for rice crop.</p><h3 class="c-article__sub-heading" id="Sec41"><span class="c-article-section__title-number">3.13 </span>Vegetation Indices Based Rice Yield Distribution Analysis</h3><p>Using the Map Query tool of the ArcGIS software platform, the areas with higher vegetation indices values (very high and high class) showed sound rice yield of > 8 ton/ha in the study area (Table <a data-track="click" data-track-label="link" data-track-action="table anchor" href="/article/10.1007/s41748-024-00481-2#Tab4">4</a>). We were able to ascertain whether there was any connection between the regions identified as having higher vegetation indices and better rice yield. Among the vegetation indices, MTVI and MSAVI had prediction accuracies above 90%, followed by NDVI, SAVI, and Green NDVI (> 89%), (Fig. <a data-track="click" data-track-label="link" data-track-action="figure anchor" href="/article/10.1007/s41748-024-00481-2#Fig8">8</a>). These five vegetation indices can be directly used to estimate crop yield from the high-resolution images provided by the PlanetScope satellite constellation.</p><div class="c-article-table" data-test="inline-table" data-container-section="table" id="table-4"><figure><figcaption class="c-article-table__figcaption"><b id="Tab4" data-test="table-caption">Table 4 Correlation of PlanetScope based vegetation indices and crop yield</b></figcaption><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="table-link" data-track="click" data-track-action="view table" data-track-label="button" rel="nofollow" href="/article/10.1007/s41748-024-00481-2/tables/4" aria-label="Full size table 4"><span>Full size table</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div><div class="c-article-section__figure js-c-reading-companion-figures-item" data-test="figure" data-container-section="figure" id="figure-8" data-title="Fig. 8"><figure><figcaption><b id="Fig8" class="c-article-section__figure-caption" data-test="figure-caption-text">Fig. 8</b></figcaption><div class="c-article-section__figure-content"><div class="c-article-section__figure-item"><a class="c-article-section__figure-link" data-test="img-link" data-track="click" data-track-label="image" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/8" rel="nofollow"><picture><source type="image/webp" srcset="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig8_HTML.png?as=webp"><img aria-describedby="Fig8" src="//media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs41748-024-00481-2/MediaObjects/41748_2024_481_Fig8_HTML.png" alt="figure 8" loading="lazy" width="685" height="1259"></picture></a></div><div class="c-article-section__figure-description" data-test="bottom-caption" id="figure-8-desc"><p>Higher vegetation indices values with sound crop yield distribution: <b>a</b> CI_Green, <b>b</b> Green_NDVI, <b>c</b> MSAVI, <b>d</b> MTVI, <b>e</b> NDVI, <b>f</b> bPVI <b>g</b> SAVI, <b>h</b> SR, <b>i</b> TSAVI, <b>j</b> VARI, and <b>k</b> rice yield. (Note: Red colored area with high vegetation indices representing sound (high and very high) rice yield)</p></div></div><div class="u-text-right u-hide-print"><a class="c-article__pill-button" data-test="article-link" data-track="click" data-track-label="button" data-track-action="view figure" href="/article/10.1007/s41748-024-00481-2/figures/8" data-track-dest="link:Figure8 Full size image" aria-label="Full size image figure 8" rel="nofollow"><span>Full size image</span><svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-chevron-right-small"></use></svg></a></div></figure></div></div></div></section><section data-title="Discussion"><div class="c-article-section" id="Sec42-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec42"><span class="c-article-section__title-number">4 </span>Discussion</h2><div class="c-article-section__content" id="Sec42-content"><h3 class="c-article__sub-heading" id="Sec43"><span class="c-article-section__title-number">4.1 </span>PlanetScope Imagery</h3><p>The substantial demand for high-resolution images for status monitoring and yield modelling has been a major drawback of optical remote sensing. Furthermore, weather-dependant remote sensing techniques further impede rainfed rice cultivation, which is primarily grown during peak rainy seasons with cloud cover. The high-resolution image provided by PlanetScope’s constellation data encourages agricultural scientists to use it for crop monitoring. The ground sample resolution for the PlanetScope constellation of Dove-C and Dove-R is 3–4.1 m, while SuperDove resolution is around 3.7 m. This allows for close monitoring of crop growth and the identification of any variation. Additionally, the PlanetScope constellation consists of approximately 130 Dove satellites, enabling imaging of nearly the entire Earth on a daily basis. Consequently, cloud-covered images can be easily disregarded for near-real-time analysis, meeting the requirement for cloud-free, high-temporal images for the continuous monitoring of biological entities such as agricultural crops.</p><h3 class="c-article__sub-heading" id="Sec44"><span class="c-article-section__title-number">4.2 </span>Machine Learning Models</h3><p>Machine learning models have a significant scope in predicting biomass and crop yield based on established crop input parameters. Overall, the hybrid and RF models demonstrated effectiveness in predicting rice yield as well as total biomass. This research utilized an RF machine learning approach, and the hybridization technique was employed to construct regional and local-scale, pixel-level yield prediction models for three wheat croplands in Southeastern Australia. Out of these three regions, namely Victoria and New South Wales, wheat yield was successfully predicted through RF (Pang et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Pang A, Chang MWL, Chen Y (2022) Evaluation of random forests (RF) for regional and local-scale wheat yield prediction in southeast Australia. Sensors 22(3):717" href="/article/10.1007/s41748-024-00481-2#ref-CR25" id="ref-link-section-d77895435e5047">2022</a>). Additionally, the cubist, enet, and hybrid models showed promising results in predicting total biomass and rice yield. The adoption of machine learning models will further streamline large-scale ground-truthing efforts, optimizing the cost and time involved in prediction studies. The widespread adoption of machine learning techniques is crucial for assessing crop status and yield at the local field scale level.</p><h3 class="c-article__sub-heading" id="Sec45"><span class="c-article-section__title-number">4.3 </span>Boruta and Multi-collinearity Evaluation of Crop Influencing Factors</h3><p>These statistical tools effectively identified the level of influence of individual factors on the final crop output, such as total biomass and rice crop yield. Based on this analysis, for quick predictions of crop yield, the number of influencing factors could be reduced, leading to a reduction in computational workload and saving total time requirements. However, considering the positive influence of pH variation on total biomass and crop yield, caution must be exercised against drawing hasty conclusions. Although the pH variation was low for a small study area, with each value below 7, fields with higher pH showed higher total biomass and crop yield. However, this trend may not hold true if the pH level exceeds 7. Alkalinity (pH > 7.0) can impair plant growth by restricting water supply to the roots, thus hindering root development (Liu et al. <a data-track="click" data-track-action="reference anchor" data-track-label="link" data-test="citation-ref" aria-label="Reference 2022" title="Liu D, Ma Y, Rui M et al (2022) Is high ph the key factor of alkali stress on plant growth and physiology? a case study with wheat (Triticum aestivum L.) seedlings. Agronomy 12(8):1820. 
 https://doi.org/10.3390/agronomy12081820
 
 " href="/article/10.1007/s41748-024-00481-2#ref-CR20" id="ref-link-section-d77895435e5058">2022</a>). Nonetheless, these statistical tools could play a significant role in selecting factors with comparatively higher levels of influence.</p><h3 class="c-article__sub-heading" id="Sec46"><span class="c-article-section__title-number">4.4 </span>Critical Analysis of Vegetation Indices</h3><p>The correlation between the vegetation indices and current crop growth parameters, particularly crop yield, was established using the Map query tool in the ArcGIS software environment. Since vegetation indices show the volume of vegetative growth, they can also be used for estimating total biomass. While total biomass can provide some returns to farmers, crop yield is still considered the primary source of income for farmers. Even the distribution maps of yield could highlight problem areas with low crop yield, prompting special attention for proper growth. With high-resolution PlanetScope data available on a daily basis, near-real-time crop monitoring will be enabled.</p><h3 class="c-article__sub-heading" id="Sec47"><span class="c-article-section__title-number">4.5 </span>Scope and Limitation</h3><p>Despite the high value of satellite remote sensing data due to their extensive coverage, spatial resolution remains as issue when estimating crop biomass and yield at the field and within-field levels. This study showed that climate, soil, and vegetation indices from PlanetScope scene during the plant growth stage could be used to predict crop biomass and yield at the field level. However, the study’s primary limitation was the unavailability of long-term crop biomass and yield data. The study only included fragmented small farms, making it challenging to analyze the data in detail. Therefore, the biomass-yield estimation methods may not be as accurate as desired. Undoubtedly, additional research is necessary to assess faster hybrid deep learning models utilizing larger datasets. More fields and multi-year biomass and yield data may be required to confirm the accuracy of the estimation. Future studies may also focus on UAV-based biomass and yield estimation and the validation of 3 m PlanetScope data.</p></div></div></section><section data-title="Conclusion"><div class="c-article-section" id="Sec48-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Sec48"><span class="c-article-section__title-number">5 </span>Conclusion</h2><div class="c-article-section__content" id="Sec48-content"><p>Along with ten vegetation indices and three Principal Component Analysis (PCA) soil nutrient levels, nearly thirty-six factors were analysed to predict rice total biomass and crop yield using ten machine learning models. Boruta and multi-collinearity analyses were also carried out for the selected factors to explore their influence level. The study area was found to have sound rice yield in the range of 5–10 t/ha following a sound biomass growth in the very fertile alluvial soil. Four ML models namely, cubist, random forest enet, and hybrid showed promising predictions with R<sup>2</sup> > 0.88. Boruta sensitive analysis found that nearly 24 individual factors showed sound level of influence on final crop yield including OC, P, EC, mechanization level along with majority of the vegetation indices. Critical analysis carried out through the Map query tool showed that five vegetation indices estimated through high-resolution PlanetScope images found to have a sound correlation (> 89%) in identifying areas with high to very high rice yield. The study can serve as guidelines for near real-time crop monitoring in the near future using high resolution PlanetScope images.</p></div></div></section> </div> <section data-title="Data Availability"><div class="c-article-section" id="data-availability-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="data-availability">Data Availability</h2><div class="c-article-section__content" id="data-availability-content"> <p>The data is available on request to the corresponding author.</p> </div></div></section><div id="MagazineFulltextArticleBodySuffix"><section aria-labelledby="Bib1" data-title="References"><div class="c-article-section" id="Bib1-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Bib1">References</h2><div class="c-article-section__content" id="Bib1-content"><div data-container-section="references"><ul class="c-article-references" data-track-component="outbound reference" data-track-context="references section"><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR313">Behzad S, Razavi M, Mahajeri M (1992) The effect of mineral nutrients (N.P.K.) on Saffron production. Acta Horticulture 306:426–430. <a href="https://doi.org/10.17660/ActaHortic.1992.306.56" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.17660/ActaHortic.1992.306.56">https://doi.org/10.17660/ActaHortic.1992.306.56</a></p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR1">Cao J, Zhang Z, Tao F, Zhang L, Luo Y, Han J, Li Z (2020) Identifying the contributions of multi-source data for winter wheat yield prediction in China. Remote Sens 12(5):750. <a href="https://doi.org/10.3390/rs12050750" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.3390/rs12050750">https://doi.org/10.3390/rs12050750</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.3390/rs12050750" data-track-item_id="10.3390/rs12050750" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.3390%2Frs12050750" aria-label="Article reference 2" data-doi="10.3390/rs12050750">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 2" href="http://scholar.google.com/scholar_lookup?&title=Identifying%20the%20contributions%20of%20multi-source%20data%20for%20winter%20wheat%20yield%20prediction%20in%20China&journal=Remote%20Sens&doi=10.3390%2Frs12050750&volume=12&issue=5&publication_year=2020&author=Cao%2CJ&author=Zhang%2CZ&author=Tao%2CF&author=Zhang%2CL&author=Luo%2CY&author=Han%2CJ&author=Li%2CZ"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR2">Cherlinka V (2024) Rice growth stages: roadmap of timely crop management. <a href="https://eos.com/crop-management-guide/rice-growth-stages/" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="https://eos.com/crop-management-guide/rice-growth-stages/">https://eos.com/crop-management-guide/rice-growth-stages/</a>. Accessed 10<sup>th</sup> Mar 2024</p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR3">Chlingaryan A, Sukkarieh S, Whelan B (2018) Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review. Comput Electron Agric 151:61–69. <a href="https://doi.org/10.1016/j.compag.2018.05.012" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.1016/j.compag.2018.05.012">https://doi.org/10.1016/j.compag.2018.05.012</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.1016/j.compag.2018.05.012" data-track-item_id="10.1016/j.compag.2018.05.012" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.1016%2Fj.compag.2018.05.012" aria-label="Article reference 4" data-doi="10.1016/j.compag.2018.05.012">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 4" href="http://scholar.google.com/scholar_lookup?&title=Machine%20learning%20approaches%20for%20crop%20yield%20prediction%20and%20nitrogen%20status%20estimation%20in%20precision%20agriculture%3A%20a%20review&journal=Comput%20Electron%20Agric&doi=10.1016%2Fj.compag.2018.05.012&volume=151&pages=61-69&publication_year=2018&author=Chlingaryan%2CA&author=Sukkarieh%2CS&author=Whelan%2CB"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR300">Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, Hoersch B, Isola C, Laberinti P, Martimort P, Meygret A, Spoto F, Sy O, Marchese F, Bargellini P (2012) Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens Environ 120:25-36. <a href="https://doi.org/10.1016/j.rse.2011.11.026" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.1016/j.rse.2011.11.026">https://doi.org/10.1016/j.rse.2011.11.026</a></p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR304">Durmmond S, Sudduth K, Buchleiter GW (2003) Soil electrical conductivity and topography related to yield for three vontrasting soil–crop systems. Agronomy J 953(3): 483-497.</p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR310">FAOSTAT (2016) Food and Agriculture Organization of the United Nations (FAO). FAOSTAT Database. <a href="http://faostat.fao.org/site/291/default.aspx" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="http://faostat.fao.org/site/291/default.aspx">http://faostat.fao.org/site/291/default.aspx</a></p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR4">Friedman J, Hastie T, Tibshirani R (2009) glmnet: Lasso and elastic-net regularized generalized linear models. R Packag Version 2009:1</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 8" href="http://scholar.google.com/scholar_lookup?&title=glmnet%3A%20Lasso%20and%20elastic-net%20regularized%20generalized%20linear%20models&journal=R%20Packag%20Version&volume=2009&publication_year=2009&author=Friedman%2CJ&author=Hastie%2CT&author=Tibshirani%2CR"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR311">Ghosh BK (2010) Determinants of farm mechanisation in modern agriculture: a case study of burdwan districts of West Bengal. Int J Agril Res 5:1107–1115.</p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR5">Glória LS, Cruz CD, Vieira RAM, de Resende MDV, Lopes PS, de Siqueira OHD et al (2016) Accessing marker effects and heritability estimates from genome prediction by bayesian regularized neural networks. Livest Sci 191:91–96. <a href="https://doi.org/10.1016/j.livsci.2016.07.015" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.1016/j.livsci.2016.07.015">https://doi.org/10.1016/j.livsci.2016.07.015</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.1016/j.livsci.2016.07.015" data-track-item_id="10.1016/j.livsci.2016.07.015" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.1016%2Fj.livsci.2016.07.015" aria-label="Article reference 10" data-doi="10.1016/j.livsci.2016.07.015">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 10" href="http://scholar.google.com/scholar_lookup?&title=Accessing%20marker%20effects%20and%20heritability%20estimates%20from%20genome%20prediction%20by%20bayesian%20regularized%20neural%20networks&journal=Livest%20Sci&doi=10.1016%2Fj.livsci.2016.07.015&volume=191&pages=91-96&publication_year=2016&author=Gl%C3%B3ria%2CLS&author=Cruz%2CCD&author=Vieira%2CRAM&author=Resende%2CMDV&author=Lopes%2CPS&author=Siqueira%2COHD"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR301">Gnyp ML, Bareth G, Li F, Lenz-Wiedemann VIS, Koppe W, Miao Y, Hennig SD, Liangliang J L, Laudien R, Xinping C X, Zhang F (2014) Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain. Int J Appl Earth Observ Geoinform 33:232–242.</p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR6">Grubinger T, Zeileis A, Pfeiffer KP (2014) evtree: Evolutionary learning of globally optimal classification and regression trees in R. J Stat Soft 61(1):1–29</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.18637/jss.v061.i01" data-track-item_id="10.18637/jss.v061.i01" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.18637%2Fjss.v061.i01" aria-label="Article reference 12" data-doi="10.18637/jss.v061.i01">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 12" href="http://scholar.google.com/scholar_lookup?&title=evtree%3A%20Evolutionary%20learning%20of%20globally%20optimal%20classification%20and%20regression%20trees%20in%20R&journal=J%20Stat%20Soft&doi=10.18637%2Fjss.v061.i01&volume=61&issue=1&pages=1-29&publication_year=2014&author=Grubinger%2CT&author=Zeileis%2CA&author=Pfeiffer%2CKP"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR500">Gyamerah S, Philip N, Dennis I (2020) Probabilistic forecasting of crop yields via quantile random forest and Epanechnikov Kernel function. Agric For Meteorol 280:107808. <a href="https://doi.org/10.1016/j.agrformet.2019.107808" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.1016/j.agrformet.2019.107808">https://doi.org/10.1016/j.agrformet.2019.107808</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.1016/j.agrformet.2019.107808" data-track-item_id="10.1016/j.agrformet.2019.107808" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.1016%2Fj.agrformet.2019.107808" aria-label="Article reference 13" data-doi="10.1016/j.agrformet.2019.107808">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 13" href="http://scholar.google.com/scholar_lookup?&title=Probabilistic%20forecasting%20of%20crop%20yields%20via%20quantile%20random%20forest%20and%20Epanechnikov%20Kernel%20function&journal=Agric%20For%20Meteorol&doi=10.1016%2Fj.agrformet.2019.107808&volume=280&publication_year=2020&author=Gyamerah%2CS&author=Philip%2CN&author=Dennis%2CI"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR7">Hastie TJ (1992) Generalized additive models. In: HastieChambers TJJM (ed) Chapter 7 of Statistical Models in S. Wadsworth & Brooks/Cole, Pacific Grove</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 14" href="http://scholar.google.com/scholar_lookup?&title=Generalized%20additive%20models&publication_year=1992&author=Hastie%2CTJ"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR8">James G, Daniela W, Trevor H, Robert T (2013) An Introduction to Statistical Learning: With Applications in R 1st ed. Springer, New York</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="noopener" data-track-label="10.1007/978-1-4614-7138-7" data-track-item_id="10.1007/978-1-4614-7138-7" data-track-value="book reference" data-track-action="book reference" href="https://link.springer.com/doi/10.1007/978-1-4614-7138-7" aria-label="Book reference 15" data-doi="10.1007/978-1-4614-7138-7">Book</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 15" href="http://scholar.google.com/scholar_lookup?&title=An%20Introduction%20to%20Statistical%20Learning%3A%20With%20Applications%20in%20R%201st%20ed&doi=10.1007%2F978-1-4614-7138-7&publication_year=2013&author=James%2CG&author=Daniela%2CW&author=Trevor%2CH&author=Robert%2CT"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR9">Jumpasut A, Fukuzato A, Zuleta I (2018) Using the moon as a calibration source for a fleet of satellites, conference on characterization and radiometric calibration for remote sensing, CALCON 2018,Utah State University, Logan, UT during 18–21 June 2018</p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR10">Kaur S, Kalsi S (2019) Analysis of wheat production using Naïve Bayes classifier. Int J Comput Appl 178(14):0975–8887</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 17" href="http://scholar.google.com/scholar_lookup?&title=Analysis%20of%20wheat%20production%20using%20Na%C3%AFve%20Bayes%20classifier&journal=Int%20J%20Comput%20Appl&volume=178&issue=14&pages=0975-8887&publication_year=2019&author=Kaur%2CS&author=Kalsi%2CS"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR11">Khaki S, Wang L (2019) Crop yield prediction using deep neural networks. Front Plant Sci 10:621</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.3389/fpls.2019.00621" data-track-item_id="10.3389/fpls.2019.00621" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.3389%2Ffpls.2019.00621" aria-label="Article reference 18" data-doi="10.3389/fpls.2019.00621">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 18" href="http://scholar.google.com/scholar_lookup?&title=Crop%20yield%20prediction%20using%20deep%20neural%20networks&journal=Front%20Plant%20Sci&doi=10.3389%2Ffpls.2019.00621&volume=10&publication_year=2019&author=Khaki%2CS&author=Wang%2CL"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR12">Khanal S, Fulton J, Klopfenstein A, Douridas N, Shearer S (2018) Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield. Comput Electron Agric 153:213–225. <a href="https://doi.org/10.1016/j.compag.2018.07.016" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.1016/j.compag.2018.07.016">https://doi.org/10.1016/j.compag.2018.07.016</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.1016/j.compag.2018.07.016" data-track-item_id="10.1016/j.compag.2018.07.016" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.1016%2Fj.compag.2018.07.016" aria-label="Article reference 19" data-doi="10.1016/j.compag.2018.07.016">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 19" href="http://scholar.google.com/scholar_lookup?&title=Integration%20of%20high%20resolution%20remotely%20sensed%20data%20and%20machine%20learning%20techniques%20for%20spatial%20prediction%20of%20soil%20properties%20and%20corn%20yield&journal=Comput%20Electron%20Agric&doi=10.1016%2Fj.compag.2018.07.016&volume=153&pages=213-225&publication_year=2018&author=Khanal%2CS&author=Fulton%2CJ&author=Klopfenstein%2CA&author=Douridas%2CN&author=Shearer%2CS"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR13">Kim N, Ha KJ, Park NW, Cho J, Hong S, Lee YW (2019) A comparison between major artificial intelligence models for crop yield prediction: case study of the midwestern United States, 2006–2015. ISPRS Int J Geo-Info 8(5):240. <a href="https://doi.org/10.3390/ijgi8050240" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.3390/ijgi8050240">https://doi.org/10.3390/ijgi8050240</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.3390/ijgi8050240" data-track-item_id="10.3390/ijgi8050240" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.3390%2Fijgi8050240" aria-label="Article reference 20" data-doi="10.3390/ijgi8050240">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 20" href="http://scholar.google.com/scholar_lookup?&title=A%20comparison%20between%20major%20artificial%20intelligence%20models%20for%20crop%20yield%20prediction%3A%20case%20study%20of%20the%20midwestern%20United%20States%2C%202006%E2%80%932015&journal=ISPRS%20Int%20J%20Geo-Info&doi=10.3390%2Fijgi8050240&volume=8&issue=5&publication_year=2019&author=Kim%2CN&author=Ha%2CKJ&author=Park%2CNW&author=Cho%2CJ&author=Hong%2CS&author=Lee%2CYW"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR302">Kotchenova SY, Vermote EF, Matarrese R, Klemm FJ Jr (2006) Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: Path radiance. Appl Opt 45:6762–6774.</p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR14">Kpienbaareh D, Mohammed K, Luginaah I, Wang J, Bezner Kerr R, Lupafya E, Dakishoni L (2022) Estimating groundnut yield in smallholder agriculture systems using PlanetScope data. Land 11:1752. <a href="https://doi.org/10.3390/land11101752" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.3390/land11101752">https://doi.org/10.3390/land11101752</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.3390/land11101752" data-track-item_id="10.3390/land11101752" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.3390%2Fland11101752" aria-label="Article reference 22" data-doi="10.3390/land11101752">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 22" href="http://scholar.google.com/scholar_lookup?&title=Estimating%20groundnut%20yield%20in%20smallholder%20agriculture%20systems%20using%20PlanetScope%20data&journal=Land&doi=10.3390%2Fland11101752&volume=11&publication_year=2022&author=Kpienbaareh%2CD&author=Mohammed%2CK&author=Luginaah%2CI&author=Wang%2CJ&author=Bezner%20Kerr%2CR&author=Lupafya%2CE&author=Dakishoni%2CL"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR15">Kuhn M, Kjell J (2016) Applied Predictive Modeling 1st ed. Springer, New York</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 23" href="http://scholar.google.com/scholar_lookup?&title=Applied%20Predictive%20Modeling%201st%20ed&publication_year=2016&author=Kuhn%2CM&author=Kjell%2CJ"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR312">Piper CS (1942) Soil and plant analysis. The university of adelaide, Adelaide, p. 368</p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR16">le Roux J, Christopher S, Maskey M (2021) Exploring the use of PlanetScope data for particulate matter air quality research. Remote Sens 13:2981. <a href="https://doi.org/10.3390/rs13152981" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.3390/rs13152981">https://doi.org/10.3390/rs13152981</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.3390/rs13152981" data-track-item_id="10.3390/rs13152981" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.3390%2Frs13152981" aria-label="Article reference 25" data-doi="10.3390/rs13152981">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 25" href="http://scholar.google.com/scholar_lookup?&title=Exploring%20the%20use%20of%20PlanetScope%20data%20for%20particulate%20matter%20air%20quality%20research&journal=Remote%20Sens&doi=10.3390%2Frs13152981&volume=13&publication_year=2021&author=Roux%2CJ&author=Christopher%2CS&author=Maskey%2CM"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR17">Leach N, Coops NC, Obrknezev N (2019) Normalization method for multi-sensor high spatial and temporal resolution satellite imagery with radiometric inconsistencies. Comput Electron Agric 164:104893. <a href="https://doi.org/10.1016/j.compag.2019.104893" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.1016/j.compag.2019.104893">https://doi.org/10.1016/j.compag.2019.104893</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.1016/j.compag.2019.104893" data-track-item_id="10.1016/j.compag.2019.104893" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.1016%2Fj.compag.2019.104893" aria-label="Article reference 26" data-doi="10.1016/j.compag.2019.104893">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 26" href="http://scholar.google.com/scholar_lookup?&title=Normalization%20method%20for%20multi-sensor%20high%20spatial%20and%20temporal%20resolution%20satellite%20imagery%20with%20radiometric%20inconsistencies&journal=Comput%20Electron%20Agric&doi=10.1016%2Fj.compag.2019.104893&volume=164&publication_year=2019&author=Leach%2CN&author=Coops%2CNC&author=Obrknezev%2CN"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR18">Li F, Miao Y, Chen X, Sun Z, Stueve K, Yuan F (2022) In-season prediction of corn grain yield through PlanetScope and sentinel-2 images. Agronomy 12:3176. <a href="https://doi.org/10.3390/agronomy12123176" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.3390/agronomy12123176">https://doi.org/10.3390/agronomy12123176</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.3390/agronomy12123176" data-track-item_id="10.3390/agronomy12123176" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.3390%2Fagronomy12123176" aria-label="Article reference 27" data-doi="10.3390/agronomy12123176">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 27" href="http://scholar.google.com/scholar_lookup?&title=In-season%20prediction%20of%20corn%20grain%20yield%20through%20PlanetScope%20and%20sentinel-2%20images&journal=Agronomy&doi=10.3390%2Fagronomy12123176&volume=12&publication_year=2022&author=Li%2CF&author=Miao%2CY&author=Chen%2CX&author=Sun%2CZ&author=Stueve%2CK&author=Yuan%2CF"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR19">Liaw A, Wiener M (2002) Classification and regression by randomforest. R News 2:18–22</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 28" href="http://scholar.google.com/scholar_lookup?&title=Classification%20and%20regression%20by%20randomforest&journal=R%20News&volume=2&pages=18-22&publication_year=2002&author=Liaw%2CA&author=Wiener%2CM"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR20">Liu D, Ma Y, Rui M et al (2022) Is high ph the key factor of alkali stress on plant growth and physiology? a case study with wheat (<i>Triticum aestivum</i> L.) seedlings. Agronomy 12(8):1820. <a href="https://doi.org/10.3390/agronomy12081820" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.3390/agronomy12081820">https://doi.org/10.3390/agronomy12081820</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.3390/agronomy12081820" data-track-item_id="10.3390/agronomy12081820" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.3390%2Fagronomy12081820" aria-label="Article reference 29" data-doi="10.3390/agronomy12081820">Article</a> <a data-track="click_references" rel="nofollow noopener" data-track-label="link" data-track-item_id="link" data-track-value="cas reference" data-track-action="cas reference" href="/articles/cas-redirect/1:CAS:528:DC%2BB38Xit1KgtLjM" aria-label="CAS reference 29">CAS</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 29" href="http://scholar.google.com/scholar_lookup?&title=Is%20high%20ph%20the%20key%20factor%20of%20alkali%20stress%20on%20plant%20growth%20and%20physiology%3F%20a%20case%20study%20with%20wheat%20%28Triticum%20aestivum%20L.%29%20seedlings&journal=Agronomy&doi=10.3390%2Fagronomy12081820&volume=12&issue=8&publication_year=2022&author=Liu%2CD&author=Ma%2CY&author=Rui%2CM"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR21">Martin CM (2009) Crop yield prediction using artificial neural networks and genetic algorithms. <a href="http://purl.galileo.usg.edu/uga_etd/martin_charles_m_200912_ms" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="http://purl.galileo.usg.edu/uga_etd/martin_charles_m_200912_ms">http://purl.galileo.usg.edu/uga_etd/martin_charles_m_200912_ms</a>, <a href="http://hdl.handle.net/10724/26098" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="http://hdl.handle.net/10724/26098">http://hdl.handle.net/10724/26098</a>. Accessed 15 Mar 2023</p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR22">Meshram V, Patil K, Meshram V, Hanchate D, Ramkteke SD (2021) Machine learning in agriculture domain: a state-of-art survey. Artif Intell Life Sci 1:100010</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="link" data-track-item_id="link" data-track-value="cas reference" data-track-action="cas reference" href="/articles/cas-redirect/1:CAS:528:DC%2BB38Xht1ylu77E" aria-label="CAS reference 31">CAS</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 31" href="http://scholar.google.com/scholar_lookup?&title=Machine%20learning%20in%20agriculture%20domain%3A%20a%20state-of-art%20survey&journal=Artif%20Intell%20Life%20Sci&volume=1&publication_year=2021&author=Meshram%2CV&author=Patil%2CK&author=Meshram%2CV&author=Hanchate%2CD&author=Ramkteke%2CSD"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR23">Nevavuori P, Narra N, Lipping T (2019) Crop yield prediction with deep convolutional neural networks. Compu Electron Agric 163:104859. <a href="https://doi.org/10.1016/j.compag.2019.104859" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.1016/j.compag.2019.104859">https://doi.org/10.1016/j.compag.2019.104859</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.1016/j.compag.2019.104859" data-track-item_id="10.1016/j.compag.2019.104859" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.1016%2Fj.compag.2019.104859" aria-label="Article reference 32" data-doi="10.1016/j.compag.2019.104859">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 32" href="http://scholar.google.com/scholar_lookup?&title=Crop%20yield%20prediction%20with%20deep%20convolutional%20neural%20networks&journal=Compu%20Electron%20Agric&doi=10.1016%2Fj.compag.2019.104859&volume=163&publication_year=2019&author=Nevavuori%2CP&author=Narra%2CN&author=Lipping%2CT"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR24">Norouzi M, Ayoubi S, Jalalian A, Khademi H, Dehghani AA (2010) Predicting rainfed wheat quality and quantity by artificial neural network using terrain and soil characteristics. Acta Agric Scand Sect B Soil Plant Sci 60(4):341–352</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="link" data-track-item_id="link" data-track-value="cas reference" data-track-action="cas reference" href="/articles/cas-redirect/1:CAS:528:DC%2BC3cXnsFGns7c%3D" aria-label="CAS reference 33">CAS</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 33" href="http://scholar.google.com/scholar_lookup?&title=Predicting%20rainfed%20wheat%20quality%20and%20quantity%20by%20artificial%20neural%20network%20using%20terrain%20and%20soil%20characteristics&journal=Acta%20Agric%20Scand%20Sect%20B%20Soil%20Plant%20Sci&volume=60&issue=4&pages=341-352&publication_year=2010&author=Norouzi%2CM&author=Ayoubi%2CS&author=Jalalian%2CA&author=Khademi%2CH&author=Dehghani%2CAA"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR25">Pang A, Chang MWL, Chen Y (2022) Evaluation of random forests (RF) for regional and local-scale wheat yield prediction in southeast Australia. Sensors 22(3):717</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.3390/s22030717" data-track-item_id="10.3390/s22030717" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.3390%2Fs22030717" aria-label="Article reference 34" data-doi="10.3390/s22030717">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 34" href="http://scholar.google.com/scholar_lookup?&title=Evaluation%20of%20random%20forests%20%28RF%29%20for%20regional%20and%20local-scale%20wheat%20yield%20prediction%20in%20southeast%20Australia&journal=Sensors&doi=10.3390%2Fs22030717&volume=22&issue=3&publication_year=2022&author=Pang%2CA&author=Chang%2CMWL&author=Chen%2CY"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR26">Pantazi XE, Moshou D, Alexandridis T, Whetton RL, Mouazen AM (2016) Wheat yield prediction using machine learning and advanced sensing techniques. Comput Elect Agricult 121:57–65. <a href="https://doi.org/10.1016/j.compag.2015.11.018" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.1016/j.compag.2015.11.018">https://doi.org/10.1016/j.compag.2015.11.018</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.1016/j.compag.2015.11.018" data-track-item_id="10.1016/j.compag.2015.11.018" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.1016%2Fj.compag.2015.11.018" aria-label="Article reference 35" data-doi="10.1016/j.compag.2015.11.018">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 35" href="http://scholar.google.com/scholar_lookup?&title=Wheat%20yield%20prediction%20using%20machine%20learning%20and%20advanced%20sensing%20techniques&journal=Comput%20Elect%20Agricult&doi=10.1016%2Fj.compag.2015.11.018&volume=121&pages=57-65&publication_year=2016&author=Pantazi%2CXE&author=Moshou%2CD&author=Alexandridis%2CT&author=Whetton%2CRL&author=Mouazen%2CAM"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR27">Quinlan JR (1993) Combining instance-based and model-based learning. In: proceedings of the 10th international conference on machine learning, Amherst, MA, USA, 27–29 June 1993, 236–243</p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR28">Singha C, Swain KC (2022) Rice and potato yield prediction using artificial intelligence technique. In: Pattnaik PK, Kumar R, Pal S (eds) Intelligent of Things, vol 3. Springer, Singapore, pp 185–199</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 37" href="http://scholar.google.com/scholar_lookup?&title=Rice%20and%20potato%20yield%20prediction%20using%20artificial%20intelligence%20technique&pages=185-199&publication_year=2022&author=Sangha%2CC&author=Swain%2CKC"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR29">Schaeffer BA, Whitman P, Conmy R, Salls W, Coffer M, Graybill D, Lebrasse MC (2022) Potential for commercial PlanetScope satellites in oil response monitoring. Marine Pollut Bulle 183:114077. <a href="https://doi.org/10.1016/j.marpolbul.2022.114077" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.1016/j.marpolbul.2022.114077">https://doi.org/10.1016/j.marpolbul.2022.114077</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.1016/j.marpolbul.2022.114077" data-track-item_id="10.1016/j.marpolbul.2022.114077" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.1016%2Fj.marpolbul.2022.114077" aria-label="Article reference 38" data-doi="10.1016/j.marpolbul.2022.114077">Article</a> <a data-track="click_references" rel="nofollow noopener" data-track-label="link" data-track-item_id="link" data-track-value="cas reference" data-track-action="cas reference" href="/articles/cas-redirect/1:CAS:528:DC%2BB38XitlCiurrN" aria-label="CAS reference 38">CAS</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 38" href="http://scholar.google.com/scholar_lookup?&title=Potential%20for%20commercial%20PlanetScope%20satellites%20in%20oil%20response%20monitoring&journal=Marine%20Pollut%20Bulle&doi=10.1016%2Fj.marpolbul.2022.114077&volume=183&publication_year=2022&author=Schaeffer%2CBA&author=Whitman%2CP&author=Conmy%2CR&author=Salls%2CW&author=Coffer%2CM&author=Graybill%2CD&author=Lebrasse%2CMC"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR30">Shakoor MT, Rahman K, Rayta SN, Chakrabarty A (2017) Agricultural production output prediction using supervised machine learning techniques. 2017 1st international conference on next generation computing applications (NextComp). <a href="https://doi.org/10.1109/nextcomp.2017.8016196" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.1109/nextcomp.2017.8016196">https://doi.org/10.1109/nextcomp.2017.8016196</a></p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR31">Singha C, Swain KC, Saren BK (2019) Land suitability assessment for potato crop using analytic hierarchy process technique and geographic information system. J Agric Engine 56(3):77–88</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 40" href="http://scholar.google.com/scholar_lookup?&title=Land%20suitability%20assessment%20for%20potato%20crop%20using%20analytic%20hierarchy%20process%20technique%20and%20geographic%20information%20system&journal=J%20Agric%20Engine&volume=56&issue=3&pages=77-88&publication_year=2019&author=Singha%2CC&author=Swain%2CKC&author=Saren%2CBK"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR32">Singha C, Swain KC, Swain SK (2020) Best crop rotation selection with GIS-AHP technique using soil nutrient variability. Agriculture 10(213):1–18</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 41" href="http://scholar.google.com/scholar_lookup?&title=Best%20crop%20rotation%20selection%20with%20GIS-AHP%20technique%20using%20soil%20nutrient%20variability&journal=Agriculture&volume=10&issue=213&pages=1-18&publication_year=2020&author=Singha%2CC&author=Swain%2CKC&author=Swain%2CSK"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR33">Singha C, Swain KC, Meliho M, Abdo HG, Almohamad H, Al-Mutiry M (2022) Spatial analysis of flood hazard zoning map using novel hybrid machine learning technique in Assam. India Remote Sens 14(24):6229. <a href="https://doi.org/10.3390/rs14246229" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.3390/rs14246229">https://doi.org/10.3390/rs14246229</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.3390/rs14246229" data-track-item_id="10.3390/rs14246229" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.3390%2Frs14246229" aria-label="Article reference 42" data-doi="10.3390/rs14246229">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 42" href="http://scholar.google.com/scholar_lookup?&title=Spatial%20analysis%20of%20flood%20hazard%20zoning%20map%20using%20novel%20hybrid%20machine%20learning%20technique%20in%20Assam&journal=India%20Remote%20Sens&doi=10.3390%2Frs14246229&volume=14&issue=24&publication_year=2022&author=Singha%2CC&author=Swain%2CKC&author=Meliho%2CM&author=Abdo%2CHG&author=Almohamad%2CH&author=Al-Mutiry%2CM"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR34">Singha C, Gulzar S, Swain KC, Pradhan D (2023) Apple yield prediction mapping using machine learning techniques through the google earth engine cloud in Kashmir valley, India. J App Remote Sens 17(1):014505. <a href="https://doi.org/10.1117/1.JRS.17.014505" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.1117/1.JRS.17.014505">https://doi.org/10.1117/1.JRS.17.014505</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.1117/1.JRS.17.014505" data-track-item_id="10.1117/1.JRS.17.014505" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.1117%2F1.JRS.17.014505" aria-label="Article reference 43" data-doi="10.1117/1.JRS.17.014505">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 43" href="http://scholar.google.com/scholar_lookup?&title=Apple%20yield%20prediction%20mapping%20using%20machine%20learning%20techniques%20through%20the%20google%20earth%20engine%20cloud%20in%20Kashmir%20valley%2C%20India&journal=J%20App%20Remote%20Sens&doi=10.1117%2F1.JRS.17.014505&volume=17&issue=1&publication_year=2023&author=Singha%2CC&author=Gulzar%2CS&author=Swain%2CKC&author=Pradhan%2CD"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR35">Skakun S, Kalecinski NI, Brown MGL, Johnson DM, Vermote EF, Roger J, Franch B (2021) Assessing within-field corn and soybean yield variability from worldView-3, planet, sentinel-2, and landsat 8 satellite imagery. Remote Sens 13:872</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.3390/rs13050872" data-track-item_id="10.3390/rs13050872" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.3390%2Frs13050872" aria-label="Article reference 44" data-doi="10.3390/rs13050872">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 44" href="http://scholar.google.com/scholar_lookup?&title=Assessing%20within-field%20corn%20and%20soybean%20yield%20variability%20from%20worldView-3%2C%20planet%2C%20sentinel-2%2C%20and%20landsat%208%20satellite%20imagery&journal=Remote%20Sens&doi=10.3390%2Frs13050872&volume=13&publication_year=2021&author=Skakun%2CS&author=Kalecinski%2CNI&author=Brown%2CMGL&author=Johnson%2CDM&author=Vermote%2CEF&author=Roger%2CJ&author=Franch%2CB"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR36">Soil Survey Staff (2000) Soil taxonomy, U.S. department, agriculture handbook, No. 436. U.S. government printing office, Washington, D.C. 754</p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR37">Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Methodol) 58:267–288</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.1111/j.2517-6161.1996.tb02080.x" data-track-item_id="10.1111/j.2517-6161.1996.tb02080.x" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.1111%2Fj.2517-6161.1996.tb02080.x" aria-label="Article reference 46" data-doi="10.1111/j.2517-6161.1996.tb02080.x">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 46" href="http://scholar.google.com/scholar_lookup?&title=Regression%20shrinkage%20and%20selection%20via%20the%20lasso&journal=J%20R%20Stat%20Soc%20Ser%20B%20%28Methodol%29&doi=10.1111%2Fj.2517-6161.1996.tb02080.x&volume=58&pages=267-288&publication_year=1996&author=Tibshirani%2CR"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR38">van Klompenburg T, Kassahun A, Catal C (2020) Crop yield prediction using machine learning: a systematic literature review. Compu Electron Agric 177:105709. <a href="https://doi.org/10.1016/j.compag.2020.105709" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.1016/j.compag.2020.105709">https://doi.org/10.1016/j.compag.2020.105709</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.1016/j.compag.2020.105709" data-track-item_id="10.1016/j.compag.2020.105709" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.1016%2Fj.compag.2020.105709" aria-label="Article reference 47" data-doi="10.1016/j.compag.2020.105709">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 47" href="http://scholar.google.com/scholar_lookup?&title=Crop%20yield%20prediction%20using%20machine%20learning%3A%20a%20systematic%20literature%20review&journal=Compu%20Electron%20Agric&doi=10.1016%2Fj.compag.2020.105709&volume=177&publication_year=2020&author=Klompenburg%2CT&author=Kassahun%2CA&author=Catal%2CC"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR39">Wicaksono P, Lazuardi W (2018) Assessment of PlanetScope images for benthic habitat and seagrass species mapping in a complex optically shallow water environment. Int J Remote Sens 39:5739–5765. <a href="https://doi.org/10.1080/01431161.2018.1506951" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.1080/01431161.2018.1506951">https://doi.org/10.1080/01431161.2018.1506951</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.1080/01431161.2018.1506951" data-track-item_id="10.1080/01431161.2018.1506951" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.1080%2F01431161.2018.1506951" aria-label="Article reference 48" data-doi="10.1080/01431161.2018.1506951">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 48" href="http://scholar.google.com/scholar_lookup?&title=Assessment%20of%20PlanetScope%20images%20for%20benthic%20habitat%20and%20seagrass%20species%20mapping%20in%20a%20complex%20optically%20shallow%20water%20environment&journal=Int%20J%20Remote%20Sens&doi=10.1080%2F01431161.2018.1506951&volume=39&pages=5739-5765&publication_year=2018&author=Wicaksono%2CP&author=Lazuardi%2CW"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR40">Yazdadi EA (2016) Landslide hazard zonation by using AHP (analytical hierarchy process) model in GIS (geographic information system) environment (case study: kordan watershed). Int J Progress Sci Technol 2(1):24–39. <a href="https://doi.org/10.52155/ijpsat.v2.1.18" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.52155/ijpsat.v2.1.18">https://doi.org/10.52155/ijpsat.v2.1.18</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.52155/ijpsat.v2.1.18" data-track-item_id="10.52155/ijpsat.v2.1.18" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.52155%2Fijpsat.v2.1.18" aria-label="Article reference 49" data-doi="10.52155/ijpsat.v2.1.18">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 49" href="http://scholar.google.com/scholar_lookup?&title=Landslide%20hazard%20zonation%20by%20using%20AHP%20%28analytical%20hierarchy%20process%29%20model%20in%20GIS%20%28geographic%20information%20system%29%20environment%20%28case%20study%3A%20kordan%20watershed%29&journal=Int%20J%20Progress%20Sci%20Technol&doi=10.52155%2Fijpsat.v2.1.18&volume=2&issue=1&pages=24-39&publication_year=2016&author=Yazdadi%2CEA"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR41">Zhang W, Quan H, Srinivasan D (2018) Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination. Energy 160:810–819</p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="nofollow noopener" data-track-label="10.1016/j.energy.2018.07.019" data-track-item_id="10.1016/j.energy.2018.07.019" data-track-value="article reference" data-track-action="article reference" href="https://doi.org/10.1016%2Fj.energy.2018.07.019" aria-label="Article reference 50" data-doi="10.1016/j.energy.2018.07.019">Article</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 50" href="http://scholar.google.com/scholar_lookup?&title=Parallel%20and%20reliable%20probabilistic%20load%20forecasting%20via%20quantile%20regression%20forest%20and%20quantile%20determination&journal=Energy&doi=10.1016%2Fj.energy.2018.07.019&volume=160&pages=810-819&publication_year=2018&author=Zhang%2CW&author=Quan%2CH&author=Srinivasan%2CD"> Google Scholar</a> </p></li><li class="c-article-references__item js-c-reading-companion-references-item"><p class="c-article-references__text" id="ref-CR42">Zhou H, Xin Y, Li S (2023) A diabetes prediction model based on Boruta feature selection and ensemble learning. BMC Bioinform 24:224. <a href="https://doi.org/10.1186/s12859-023-05300-5" data-track="click_references" data-track-action="external reference" data-track-value="external reference" data-track-label="10.1186/s12859-023-05300-5">https://doi.org/10.1186/s12859-023-05300-5</a></p><p class="c-article-references__links u-hide-print"><a data-track="click_references" rel="noopener" data-track-label="10.1186/s12859-023-05300-5" data-track-item_id="10.1186/s12859-023-05300-5" data-track-value="article reference" data-track-action="article reference" href="https://link.springer.com/doi/10.1186/s12859-023-05300-5" aria-label="Article reference 51" data-doi="10.1186/s12859-023-05300-5">Article</a> <a data-track="click_references" rel="nofollow noopener" data-track-label="link" data-track-item_id="link" data-track-value="cas reference" data-track-action="cas reference" href="/articles/cas-redirect/1:CAS:528:DC%2BB3sXht1SitrnL" aria-label="CAS reference 51">CAS</a> <a data-track="click_references" data-track-action="google scholar reference" data-track-value="google scholar reference" data-track-label="link" data-track-item_id="link" rel="nofollow noopener" aria-label="Google Scholar reference 51" href="http://scholar.google.com/scholar_lookup?&title=A%20diabetes%20prediction%20model%20based%20on%20Boruta%20feature%20selection%20and%20ensemble%20learning&journal=BMC%20Bioinform&doi=10.1186%2Fs12859-023-05300-5&volume=24&publication_year=2023&author=Zhou%2CH&author=Xin%2CY&author=Li%2CS"> Google Scholar</a> </p></li></ul><p class="c-article-references__download u-hide-print"><a data-track="click" data-track-action="download citation references" data-track-label="link" rel="nofollow" href="https://citation-needed.springer.com/v2/references/10.1007/s41748-024-00481-2?format=refman&flavour=references">Download references<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-download-medium"></use></svg></a></p></div></div></div></section></div><section data-title="Funding"><div class="c-article-section" id="Fun-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="Fun">Funding</h2><div class="c-article-section__content" id="Fun-content"><p>Open Access funding enabled and organized by CAUL and its Member Institutions. This research was funded by the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney.</p></div></div></section><section aria-labelledby="author-information" data-title="Author information"><div class="c-article-section" id="author-information-section"><h2 class="c-article-section__title js-section-title js-c-reading-companion-sections-item" id="author-information">Author information</h2><div class="c-article-section__content" id="author-information-content"><h3 class="c-article__sub-heading" id="affiliations">Authors and Affiliations</h3><ol class="c-article-author-affiliation__list"><li id="Aff1"><p class="c-article-author-affiliation__address">Department of Agricultural Engineering, Institute of Agriculture, Visva-Bharati (A Central University), Santiniketan, Birbhum, Bolpur, West Bengal, 731235, India</p><p class="c-article-author-affiliation__authors-list">Kishore Chandra Swain & Chiranjit Singha</p></li><li id="Aff2"><p class="c-article-author-affiliation__address">Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, 2007, Australia</p><p class="c-article-author-affiliation__authors-list">Biswajeet Pradhan</p></li></ol><div class="u-js-hide u-hide-print" data-test="author-info"><span class="c-article__sub-heading">Authors</span><ol class="c-article-authors-search u-list-reset"><li id="auth-Kishore_Chandra-Swain-Aff1"><span class="c-article-authors-search__title u-h3 js-search-name">Kishore Chandra Swain</span><div class="c-article-authors-search__list"><div class="c-article-authors-search__item c-article-authors-search__list-item--left"><a href="/search?dc.creator=Kishore%20Chandra%20Swain" class="c-article-button" data-track="click" data-track-action="author link - 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id="citeas">Cite this article</h3><p class="c-bibliographic-information__citation">Swain, K.C., Singha, C. & Pradhan, B. Estimating Total Rice Biomass and Crop Yield at Field Scale Using PlanetScope Imagery Through Hybrid Machine Learning Models. <i>Earth Syst Environ</i> <b>8</b>, 1713–1731 (2024). https://doi.org/10.1007/s41748-024-00481-2</p><p class="c-bibliographic-information__download-citation u-hide-print"><a data-test="citation-link" data-track="click" data-track-action="download article citation" data-track-label="link" data-track-external="" rel="nofollow" href="https://citation-needed.springer.com/v2/references/10.1007/s41748-024-00481-2?format=refman&flavour=citation">Download citation<svg width="16" height="16" focusable="false" role="img" aria-hidden="true" class="u-icon"><use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#icon-eds-i-download-medium"></use></svg></a></p><ul class="c-bibliographic-information__list" data-test="publication-history"><li class="c-bibliographic-information__list-item"><p>Received<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2024-07-08">08 July 2024</time></span></p></li><li class="c-bibliographic-information__list-item"><p>Revised<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2024-09-11">11 September 2024</time></span></p></li><li class="c-bibliographic-information__list-item"><p>Accepted<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2024-09-20">20 September 2024</time></span></p></li><li class="c-bibliographic-information__list-item"><p>Published<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2024-10-18">18 October 2024</time></span></p></li><li class="c-bibliographic-information__list-item"><p>Issue Date<span class="u-hide">: </span><span class="c-bibliographic-information__value"><time datetime="2024-12">December 2024</time></span></p></li><li class="c-bibliographic-information__list-item 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