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disease identification</title> <meta name="description" content="Search results for: plant disease identification"> <meta name="keywords" content="plant disease identification"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="plant disease identification"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 9572</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: plant disease identification</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9572</span> Investigating the Factors Affecting Generalization of Deep Learning Models for Plant Disease Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Praveen%20S.%20Muthukumarana">Praveen S. Muthukumarana</a>, <a href="https://publications.waset.org/abstracts/search?q=Achala%20C.%20Aponso"> Achala C. Aponso</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A large percentage of global crop harvest is lost due to crop diseases. Timely identification and treatment of crop diseases is difficult in many developing nations due to insufficient trained professionals in the field of agriculture. Many crop diseases can be accurately diagnosed by visual symptoms. In the past decade, deep learning has been successfully utilized in domains such as healthcare but adoption in agriculture for plant disease detection is rare. The literature shows that models trained with popular datasets such as PlantVillage does not generalize well on real world images. This paper attempts to find out how to make plant disease identification models that generalize well with real world images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agriculture" title="agriculture">agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=plant%20disease%20classification" title=" plant disease classification"> plant disease classification</a>, <a href="https://publications.waset.org/abstracts/search?q=plant%20disease%20detection" title=" plant disease detection"> plant disease detection</a>, <a href="https://publications.waset.org/abstracts/search?q=plant%20disease%20diagnosis" title=" plant disease diagnosis"> plant disease diagnosis</a> </p> <a href="https://publications.waset.org/abstracts/127286/investigating-the-factors-affecting-generalization-of-deep-learning-models-for-plant-disease-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127286.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">145</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9571</span> Comparison of Deep Convolutional Neural Networks Models for Plant Disease Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Megha%20Gupta">Megha Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Nupur%20Prakash"> Nupur Prakash</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=comparative%20analysis" title="comparative analysis">comparative analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=plant%20disease%20identification" title=" plant disease identification"> plant disease identification</a> </p> <a href="https://publications.waset.org/abstracts/138543/comparison-of-deep-convolutional-neural-networks-models-for-plant-disease-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138543.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">199</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9570</span> Plant Disease Detection Using Image Processing and Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanskar">Sanskar</a>, <a href="https://publications.waset.org/abstracts/search?q=Abhinav%20Pal"> Abhinav Pal</a>, <a href="https://publications.waset.org/abstracts/search?q=Aryush%20Gupta"> Aryush Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Sushil%20Kumar%20Mishra"> Sushil Kumar Mishra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the critical and tedious assignments in agricultural practices is the detection of diseases on vegetation. Agricultural production is very important in today’s economy because plant diseases are common, and early detection of plant diseases is important in agriculture. Automatic detection of such early diseases is useful because it reduces control efforts in large productive farms. Using digital image processing and machine learning algorithms, this paper presents a method for plant disease detection. Detection of the disease occurs on different leaves of the plant. The proposed system for plant disease detection is simple and computationally efficient, requiring less time than learning-based approaches. The accuracy of various plant and foliar diseases is calculated and presented in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=plant%20diseases" title="plant diseases">plant diseases</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/194420/plant-disease-detection-using-image-processing-and-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194420.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">10</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9569</span> Multi-Labeled Aromatic Medicinal Plant Image Classification Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tsega%20Asresa">Tsega Asresa</a>, <a href="https://publications.waset.org/abstracts/search?q=Getahun%20Tigistu"> Getahun Tigistu</a>, <a href="https://publications.waset.org/abstracts/search?q=Melaku%20Bayih"> Melaku Bayih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aromatic%20medicinal%20plant" title="aromatic medicinal plant">aromatic medicinal plant</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=plant%20classification" title=" plant classification"> plant classification</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20neural%20network" title=" residual neural network"> residual neural network</a> </p> <a href="https://publications.waset.org/abstracts/175749/multi-labeled-aromatic-medicinal-plant-image-classification-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175749.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">186</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9568</span> Parkinson's Disease Gene Identification Using Physicochemical Properties of Amino Acids</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Priya%20Arora">Priya Arora</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashutosh%20Mishra"> Ashutosh Mishra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gene identification, towards the pursuit of mutated genes, leading to Parkinson’s disease, puts forward a challenge towards proactive cure of the disorder itself. Computational analysis is an effective technique for exploring genes in the form of protein sequences, as the theoretical and manual analysis is infeasible. The limitations and effectiveness of a particular computational method are entirely dependent on the previous data that is available for disease identification. The article presents a sequence-based classification method for the identification of genes responsible for Parkinson’s disease. During the initiation phase, the physicochemical properties of amino acids transform protein sequences into a feature vector. The second phase of the method employs Jaccard distances to select negative genes from the candidate population. The third phase involves artificial neural networks for making final predictions. The proposed approach is compared with the state of art methods on the basis of F-measure. The results confirm and estimate the efficiency of the method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disease%20gene%20identification" title="disease gene identification">disease gene identification</a>, <a href="https://publications.waset.org/abstracts/search?q=Parkinson%E2%80%99s%20disease" title=" Parkinson’s disease"> Parkinson’s disease</a>, <a href="https://publications.waset.org/abstracts/search?q=physicochemical%20properties%20of%20amino%20acid" title=" physicochemical properties of amino acid"> physicochemical properties of amino acid</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20sequences" title=" protein sequences"> protein sequences</a> </p> <a href="https://publications.waset.org/abstracts/116365/parkinsons-disease-gene-identification-using-physicochemical-properties-of-amino-acids" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116365.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">140</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9567</span> Functional Relevance of Flavanones and Other Plant Products in the Remedy of Parkinson's Disease</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Himanshi%20Allahabadi">Himanshi Allahabadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Plants have found a widespread use in medicine traditionally, including the treatment of cognitive disorders, especially, neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease. In terms of indigenous medicine, it has been found that many potential drugs can be isolated from plant products, including those for dementia. Plant product is widely distributed in plant kingdom and forms a major antioxidant source in the human diet, is Polyphenols. There are four important groups of polyphenols: phenolic acids, flavonoids, stilbenes, and lignans. Due to their high antioxidant capacity, interest in their study has greatly increased. There are several methods for discovering and characterizing active compounds isolated from plant sources, now available. The results obtained so far seem fulfilling, but additionally, mechanism of functioning of polyphenols at the molecular level, as well as their application in human health need to be researched upon. Also, even though the neuroprotective effects of flavonoids have been much talked about, much of the data in support of this statement has come from animal studies rather than human studies. This review is based on a multi-faceted study of medicinal plants, i.e. phytochemicals, with special focus on flavanones and their relevance in remedy of Parkinson's disease. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dementia" title="dementia">dementia</a>, <a href="https://publications.waset.org/abstracts/search?q=parkinson%27s%20disease" title=" parkinson's disease"> parkinson's disease</a>, <a href="https://publications.waset.org/abstracts/search?q=flavanones" title=" flavanones"> flavanones</a>, <a href="https://publications.waset.org/abstracts/search?q=polyphenols" title=" polyphenols"> polyphenols</a>, <a href="https://publications.waset.org/abstracts/search?q=substantia%20nigra" title=" substantia nigra"> substantia nigra</a> </p> <a href="https://publications.waset.org/abstracts/4971/functional-relevance-of-flavanones-and-other-plant-products-in-the-remedy-of-parkinsons-disease" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4971.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">307</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9566</span> IoT-Based Early Identification of Guava (Psidium guajava) Leaves and Fruits Diseases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daudi%20S.%20Simbeye">Daudi S. Simbeye</a>, <a href="https://publications.waset.org/abstracts/search?q=Mbazingwa%20E.%20Mkiramweni"> Mbazingwa E. Mkiramweni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Plant diseases have the potential to drastically diminish the quantity and quality of agricultural products. Guava (Psidium guajava), sometimes known as the apple of the tropics, is one of the most widely cultivated fruits in tropical regions. Monitoring plant health and diagnosing illnesses is an essential matter for sustainable agriculture, requiring the inspection of visually evident patterns on plant leaves and fruits. Due to minor variations in the symptoms of various guava illnesses, a professional opinion is required for disease diagnosis. Due to improper pesticide application by farmers, erroneous diagnoses may result in economic losses. This study proposes a method that uses artificial intelligence (AI) to detect and classify the most widespread guava plant by comparing images of its leaves and fruits to datasets. ESP32 CAM is responsible for data collection, which includes images of guava leaves and fruits. By comparing the datasets, these image formats are used as datasets to help in the diagnosis of plant diseases through the leaves and fruits, which is vital for the development of an effective automated agricultural system. The system test yielded the most accurate identification findings (99 percent accuracy in differentiating four guava fruit diseases (Canker, Mummification, Dot, and Rust) from healthy fruit). The proposed model has been interfaced with a mobile application to be used by smartphones to make a quick and responsible judgment, which can help the farmers instantly detect and prevent future production losses by enabling them to take precautions beforehand. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=early%20identification" title="early identification">early identification</a>, <a href="https://publications.waset.org/abstracts/search?q=guava%20plants" title=" guava plants"> guava plants</a>, <a href="https://publications.waset.org/abstracts/search?q=fruit%20diseases" title=" fruit diseases"> fruit diseases</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/161619/iot-based-early-identification-of-guava-psidium-guajava-leaves-and-fruits-diseases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161619.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">76</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9565</span> Plant Leaf Recognition Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aadhya%20Kaul">Aadhya Kaul</a>, <a href="https://publications.waset.org/abstracts/search?q=Gautam%20Manocha"> Gautam Manocha</a>, <a href="https://publications.waset.org/abstracts/search?q=Preeti%20Nagrath"> Preeti Nagrath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our environment comprises of a wide variety of plants that are similar to each other and sometimes the similarity between the plants makes the identification process tedious thus increasing the workload of the botanist all over the world. Now all the botanists cannot be accessible all the time for such laborious plant identification; therefore, there is an urge for a quick classification model. Also, along with the identification of the plants, it is also necessary to classify the plant as healthy or not as for a good lifestyle, humans require good food and this food comes from healthy plants. A large number of techniques have been applied to classify the plants as healthy or diseased in order to provide the solution. This paper proposes one such method known as anomaly detection using autoencoders using a set of collections of leaves. In this method, an autoencoder model is built using Keras and then the reconstruction of the original images of the leaves is done and the threshold loss is found in order to classify the plant leaves as healthy or diseased. A dataset of plant leaves is considered to judge the reconstructed performance by convolutional autoencoders and the average accuracy obtained is 71.55% for the purpose. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20autoencoder" title="convolutional autoencoder">convolutional autoencoder</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20application" title=" web application"> web application</a>, <a href="https://publications.waset.org/abstracts/search?q=FLASK" title=" FLASK"> FLASK</a> </p> <a href="https://publications.waset.org/abstracts/143084/plant-leaf-recognition-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143084.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9564</span> An Image Processing Scheme for Skin Fungal Disease Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20M.%20A.%20S.%20S.%20Perera">A. A. M. A. S. S. Perera</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20A.%20Ranasinghe"> L. A. Ranasinghe</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20K.%20H.%20Nimeshika"> T. K. H. Nimeshika</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20M.%20Dhanushka%20Dissanayake"> D. M. Dhanushka Dissanayake</a>, <a href="https://publications.waset.org/abstracts/search?q=Namalie%20Walgampaya"> Namalie Walgampaya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, skin fungal diseases are mostly found in people of tropical countries like Sri Lanka. A skin fungal disease is a particular kind of illness caused by fungus. These diseases have various dangerous effects on the skin and keep on spreading over time. It becomes important to identify these diseases at their initial stage to control it from spreading. This paper presents an automated skin fungal disease identification system implemented to speed up the diagnosis process by identifying skin fungal infections in digital images. An image of the diseased skin lesion is acquired and a comprehensive computer vision and image processing scheme is used to process the image for the disease identification. This includes colour analysis using RGB and HSV colour models, texture classification using Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix and Local Binary Pattern, Object detection, Shape Identification and many more. This paper presents the approach and its outcome for identification of four most common skin fungal infections, namely, Tinea Corporis, Sporotrichosis, Malassezia and Onychomycosis. The main intention of this research is to provide an automated skin fungal disease identification system that increase the diagnostic quality, shorten the time-to-diagnosis and improve the efficiency of detection and successful treatment for skin fungal diseases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Circularity%20Index" title="Circularity Index">Circularity Index</a>, <a href="https://publications.waset.org/abstracts/search?q=Grey%20Level%20Run%20Length%20Matrix" title=" Grey Level Run Length Matrix"> Grey Level Run Length Matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=Grey%20Level%20Co-Occurrence%20Matrix" title=" Grey Level Co-Occurrence Matrix"> Grey Level Co-Occurrence Matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=Local%20Binary%20Pattern" title=" Local Binary Pattern"> Local Binary Pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=Object%20detection" title=" Object detection"> Object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Ring%20Detection" title=" Ring Detection"> Ring Detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Shape%20Identification" title=" Shape Identification"> Shape Identification</a> </p> <a href="https://publications.waset.org/abstracts/82490/an-image-processing-scheme-for-skin-fungal-disease-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82490.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">232</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9563</span> Effect of Abiotic Factors on Population of Red Cotton Bug Dysdercus Koenigii F. (Heteroptera: Pyrrhocoridae) and Its Impact on Cotton Boll Disease</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haider%20Karar">Haider Karar</a>, <a href="https://publications.waset.org/abstracts/search?q=Saghir%20Ahmad"> Saghir Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Amjad%20Ali"> Amjad Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Ibrar%20Ul%20Haq"> Ibrar Ul Haq</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The experiment was conducted at Cotton Research Station, Multan to study the impact of weather factors and red cotton bug (RCB) on cotton boll disease yielded yellowish lint during 2012. The population on RCB along with abiotic factors was recorded during three consecutive years i.e. 2012, 2013, and 2014. Along with population of RCB and abiotic factors, the number of unopened/opened cotton bolls (UOB), percent yellowish lint (YL) and whitish lint (WL) were also recorded. The data revealed that the population per plant of RCB remain 0.50 and 0.34 during years 2012, 2013 but increased during 2014 i.e. 3.21 per plant. The number of UOB were more i.e. 13.43% in 2012 with YL 76.30 and WL 23.70% when average maximum temperature 34.73◦C, minimum temperature 22.83◦C, RH 77.43% and 11.08 mm rainfall. Similarly in 2013 the number of UOB were less i.e. 0.34 per plant with YL 1.48 and WL 99.53 per plant when average maximum temperature 34.60◦C, minimum temperature 23.37◦C, RH 73.01% and 9.95 mm rainfall. During 2014 RCB population per plant was 3.22 with no UOB and YL was 0.00% and WL was 100% when average maximum temperature 23.70◦C, minimum temperature 23.18◦C, RH 71.67% and 4.55 mm rainfall. So it is concluded that the cotton bolls disease was more during 2012 due to more rainfall and more percent RH. The RCB may be the carrier of boll rot disease pathogen during more rainfall. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=red%20cotton%20bug" title="red cotton bug">red cotton bug</a>, <a href="https://publications.waset.org/abstracts/search?q=cotton" title=" cotton"> cotton</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20factors" title=" weather factors"> weather factors</a>, <a href="https://publications.waset.org/abstracts/search?q=years" title=" years"> years</a> </p> <a href="https://publications.waset.org/abstracts/27283/effect-of-abiotic-factors-on-population-of-red-cotton-bug-dysdercus-koenigii-f-heteroptera-pyrrhocoridae-and-its-impact-on-cotton-boll-disease" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27283.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">345</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9562</span> Identification of Dynamic Friction Model for High-Precision Motion Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Martin%20Goubej">Martin Goubej</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomas%20Popule"> Tomas Popule</a>, <a href="https://publications.waset.org/abstracts/search?q=Alois%20Krejci"> Alois Krejci</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with experimental identification of mechanical systems with nonlinear friction characteristics. Dynamic LuGre friction model is adopted and a systematic approach to parameter identification of both linear and nonlinear subsystems is given. The identification procedure consists of three subsequent experiments which deal with the individual parts of plant dynamics. The proposed method is experimentally verified on an industrial-grade robotic manipulator. Model fidelity is compared with the results achieved with a static friction model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mechanical%20friction" title="mechanical friction">mechanical friction</a>, <a href="https://publications.waset.org/abstracts/search?q=LuGre%20model" title=" LuGre model"> LuGre model</a>, <a href="https://publications.waset.org/abstracts/search?q=friction%20identification" title=" friction identification"> friction identification</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20control" title=" motion control"> motion control</a> </p> <a href="https://publications.waset.org/abstracts/51897/identification-of-dynamic-friction-model-for-high-precision-motion-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51897.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">413</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9561</span> Diversity, Phyto Beneficial Activities and Agrobiotechnolody of Plant Growth Promoting Bacillus and Paenibacillus</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cheba%20Ben%20Amar">Cheba Ben Amar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bacillus and Paenibacillus are Gram-positive aerobic endospore-forming bacteria (AEFB) and most abundant in the rhizosphere, they mediated plant growth promotion and disease protection by several complex and interrelated processes involving direct and indirect mechanisms that include nitrogen fixation, phosphate solubilization, siderophores production, phytohormones production and plant diseases control. In addition to their multiple PGPR properties, high secretory capacity, spore forming ability and spore resistance to unfavorable conditions enabling their extended commercial applications for long shelf-life. Due to these unique advantages, Bacillus species were the most an ideal candidate for developing efficient PGPR products such as biopesticides, fungicides and fertilizers. This review list all studied and reported plant growth promoting Bacillus species and strains, discuss their capacities to enhance plant growth and protection with special focusing on the most frequent species Bacillus subtilis, B. pumilus ,B. megaterium, B. amyloliquefaciens , B. licheniformis and B. sphaericus, furthermore we recapitulate the beneﬁcial activities and mechanisms of several species and strains of the genus Paenibacillus involved in plant growth stimulation and plant disease control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bacillus" title="bacillus">bacillus</a>, <a href="https://publications.waset.org/abstracts/search?q=paenibacillus" title=" paenibacillus"> paenibacillus</a>, <a href="https://publications.waset.org/abstracts/search?q=PGPR" title=" PGPR"> PGPR</a>, <a href="https://publications.waset.org/abstracts/search?q=bene%EF%AC%81cial%20activities" title=" beneﬁcial activities"> beneﬁcial activities</a>, <a href="https://publications.waset.org/abstracts/search?q=mechanisms" title=" mechanisms"> mechanisms</a>, <a href="https://publications.waset.org/abstracts/search?q=growth%20promotion" title=" growth promotion"> growth promotion</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20control" title=" disease control"> disease control</a>, <a href="https://publications.waset.org/abstracts/search?q=agrobiotechnology" title=" agrobiotechnology"> agrobiotechnology</a> </p> <a href="https://publications.waset.org/abstracts/37958/diversity-phyto-beneficial-activities-and-agrobiotechnolody-of-plant-growth-promoting-bacillus-and-paenibacillus" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37958.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">400</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9560</span> Integrated Management of Diseases of Vegetables and Flower Crops Grown in Protected Condition under Organic Production System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shripad%20Kulkarni">Shripad Kulkarni </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Plant disease is an impairment of the normal state of a plant that interrupts or modifies its vital functions. Disease occurs on different parts of plants and cause heavy losses. Diagnosis of Problem is very important before planning any management practice and this can be done based on appearance of the crop, examination of the root and examination of the soil. There are various types of diseases such as biotic (transmissible) which accounts for ~30% whereas , abiotic (not transmissible) diseases are the major one with ~70% incidence. Plant diseases caused by different groups of organism’s belonging fungi, bacteria, viruses, nematodes and few others have remained important in causing significant losses in different crops indicating the urgent need of their integrated management. Various factors favor disease development and different steps and methods are involved in management of diseases under protected condition. Management of diseases through botanicals and bioagents by modifying root and aerial environment, vector management along with care to be taken while managing the disease are analysed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=organic%20production%20system" title="organic production system">organic production system</a>, <a href="https://publications.waset.org/abstracts/search?q=diseases" title=" diseases"> diseases</a>, <a href="https://publications.waset.org/abstracts/search?q=bioagents%20and%20polyhouse" title=" bioagents and polyhouse"> bioagents and polyhouse</a>, <a href="https://publications.waset.org/abstracts/search?q=agriculture" title=" agriculture"> agriculture</a> </p> <a href="https://publications.waset.org/abstracts/30118/integrated-management-of-diseases-of-vegetables-and-flower-crops-grown-in-protected-condition-under-organic-production-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30118.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">406</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9559</span> Morphological and Biological Identification of Fusarium Species Associated with Ear Rot Disease of Maize in Indonesia and Malaysia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Darnetty%20Baharuddin%20Salleh">Darnetty Baharuddin Salleh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fusarium ear rot disease is one of the most important diseases of maize and not only causes significant losses but also produced harmful mycotoxins to animals and humans. A total of 141 strains of Fusarium species were isolated from maize plants showing typical ear rot symptoms in Indonesia, and Malaysia by using the semi-selective medium (peptone pentachloronitrobenzene agar, PPA). These strains were identified morphologically. For strains in Gibberella fujikuroi species complex (Gfsc), the identification was continued by using biological identification. Three species of Fusarium were morphologically identified as Fusarium in Gibberella species complex (105 strains, 74.5%), F. verticillioides (78 strains), F. proliferatum (24 strains) and F. subglutinans (3 strains) and five species from other section (36 strains, 25.5%), F. graminearum (14 strains), F. oxysporum (8 strains), F. solani ( 1 strain), and F. semitectum (13 strains). Out of 105 Fusarium species in Gfsc, 63 strains were identified as MAT-1, 25 strains as MAT-2 and 17 strains could not be identified and in crosses with nine standard testers, three mating populations of Fusarium were identified as MP-A, G. moniliformis (68 strains, 64.76%), MP-D, G. intermedia (21 strains, 20%) and MP-E, G. subglutinans (3 strains, 2.9%), and 13 strains (12.38%) could not be identified. All trains biologically identified as MP-A, MP-D, and MP-E, were identified morphologically as F. verticillioides, F. proliferatum, and F. subglutinans, respectively. Thus, the results of this study indicated that identification based on biological identification were consistent with those of morphological identification. This is the first report on the presence of MP-A, MP-D, and MP-E on ear rot-infected maize in Indonesia; MP-A and MP-E in Malaysia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fusarium" title="Fusarium">Fusarium</a>, <a href="https://publications.waset.org/abstracts/search?q=MAT-1" title=" MAT-1"> MAT-1</a>, <a href="https://publications.waset.org/abstracts/search?q=MAT-2" title=" MAT-2"> MAT-2</a>, <a href="https://publications.waset.org/abstracts/search?q=MP-A" title=" MP-A"> MP-A</a>, <a href="https://publications.waset.org/abstracts/search?q=MP-D" title=" MP-D"> MP-D</a>, <a href="https://publications.waset.org/abstracts/search?q=MP-E" title=" MP-E"> MP-E</a> </p> <a href="https://publications.waset.org/abstracts/37088/morphological-and-biological-identification-of-fusarium-species-associated-with-ear-rot-disease-of-maize-in-indonesia-and-malaysia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37088.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">310</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9558</span> Automatic Detection of Sugarcane Diseases: A Computer Vision-Based Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Himanshu%20Sharma">Himanshu Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Karthik%20Kumar"> Karthik Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Harish%20Kumar"> Harish Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The major problem in crop cultivation is the occurrence of multiple crop diseases. During the growth stage, timely identification of crop diseases is paramount to ensure the high yield of crops, lower production costs, and minimize pesticide usage. In most cases, crop diseases produce observable characteristics and symptoms. The Surveyors usually diagnose crop diseases when they walk through the fields. However, surveyor inspections tend to be biased and error-prone due to the nature of the monotonous task and the subjectivity of individuals. In addition, visual inspection of each leaf or plant is costly, time-consuming, and labour-intensive. Furthermore, the plant pathologists and experts who can often identify the disease within the plant according to their symptoms in early stages are not readily available in remote regions. Therefore, this study specifically addressed early detection of leaf scald, red rot, and eyespot types of diseases within sugarcane plants. The study proposes a computer vision-based approach using a convolutional neural network (CNN) for automatic identification of crop diseases. To facilitate this, firstly, images of sugarcane diseases were taken from google without modifying the scene, background, or controlling the illumination to build the training dataset. Then, the testing dataset was developed based on the real-time collected images from the sugarcane field from India. Then, the image dataset is pre-processed for feature extraction and selection. Finally, the CNN-based Visual Geometry Group (VGG) model was deployed on the training and testing dataset to classify the images into diseased and healthy sugarcane plants and measure the model's performance using various parameters, i.e., accuracy, sensitivity, specificity, and F1-score. The promising result of the proposed model lays the groundwork for the automatic early detection of sugarcane disease. The proposed research directly sustains an increase in crop yield. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20classification" title="automatic classification">automatic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=sugarcane%20disease" title=" sugarcane disease"> sugarcane disease</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20geometry%20group" title=" visual geometry group"> visual geometry group</a> </p> <a href="https://publications.waset.org/abstracts/147383/automatic-detection-of-sugarcane-diseases-a-computer-vision-based-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147383.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">116</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9557</span> Self-Tuning Robot Control Based on Subspace Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mathias%20Marquardt">Mathias Marquardt</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20D%C3%BCnow"> Peter Dünow</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandra%20Ba%C3%9Fler"> Sandra Baßler</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper describes the use of subspace based identification methods for auto tuning of a state space control system. The plant is an unstable but self balancing transport robot. Because of the unstable character of the process it has to be identified from closed loop input-output data. Based on the identified model a state space controller combined with an observer is calculated. The subspace identification algorithm and the controller design procedure is combined to a auto tuning method. The capability of the approach was verified in a simulation experiments under different process conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto%20tuning" title="auto tuning">auto tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=balanced%20robot" title=" balanced robot"> balanced robot</a>, <a href="https://publications.waset.org/abstracts/search?q=closed%20loop%20identification" title=" closed loop identification"> closed loop identification</a>, <a href="https://publications.waset.org/abstracts/search?q=subspace%20identification" title=" subspace identification"> subspace identification</a> </p> <a href="https://publications.waset.org/abstracts/49108/self-tuning-robot-control-based-on-subspace-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49108.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">380</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9556</span> Nucleotide Based Validation of the Endangered Plant Diospyros mespiliformis (Ebenaceae) by Evaluating Short Sequence Region of Plastid rbcL Gene</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Alaklabi">Abdullah Alaklabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ibrahim%20A.%20Arif"> Ibrahim A. Arif</a>, <a href="https://publications.waset.org/abstracts/search?q=Sameera%20O.%20Bafeel"> Sameera O. Bafeel</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20H.%20Alfarhan"> Ahmad H. Alfarhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Anis%20Ahamed"> Anis Ahamed</a>, <a href="https://publications.waset.org/abstracts/search?q=Jacob%20Thomas"> Jacob Thomas</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20A.%20Bakir"> Mohammad A. Bakir </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diospyros mespiliformis (Hochst. ex A.DC.; Ebenaceae) is a large deciduous medicinal plant. This plant species is currently listed as endangered in Saudi Arabia. Molecular identification of this plant species based on short sequence regions (571 and 664 bp) of plastid rbcL (ribulose-1, 5-biphosphate carboxylase) gene was investigated in this study. The endangered plant specimens were collected from Al-Baha, Saudi Arabia (GPS coordinate: 19.8543987, 41.3059349). Phylogenetic tree inferred from the rbcL gene sequences showed that this species is very closely related with D. brandisiana. The close relationship was also observed among D. bejaudii, D. Philippinensis and D. releyi (≥99.7% sequence homology). The partial rbcL gene sequence region (571 bp) that was amplified by rbcL primer-pair rbcLaF-rbcLaR failed to discriminate D. mespiliformis from the closely related plant species, D. brandisiana. In contrast, primer-pair rbcL1F-rbcL724R yielded longer amplicon, discriminated the species from D. brandisiana and demonstrated nucleotide variations in 3 different sites (645G>T; 663A>C; 710C>G). Although D. mespiliformis (EU980712) and D. brandisiana (EU980656) are very closely related species (99.4%); however, studied specimen showed 100% sequence homology with D. mespiliformis and 99.6% with D. brandisiana. The present findings showed that rbcL short sequence region (664 bp) of plastid rbcL gene, amplified by primer-pair rbcL1F-rbcL724R, can be used for authenticating samples of D. mespiliforformis and may provide help in authentic identification and management process of this medicinally valuable endangered plant species. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diospyros%20mespiliformis" title="Diospyros mespiliformis">Diospyros mespiliformis</a>, <a href="https://publications.waset.org/abstracts/search?q=endangered%20plant" title=" endangered plant"> endangered plant</a>, <a href="https://publications.waset.org/abstracts/search?q=identification%20partial%20rbcL" title=" identification partial rbcL"> identification partial rbcL</a> </p> <a href="https://publications.waset.org/abstracts/15990/nucleotide-based-validation-of-the-endangered-plant-diospyros-mespiliformis-ebenaceae-by-evaluating-short-sequence-region-of-plastid-rbcl-gene" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15990.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9555</span> Genome-Wide Identification and Characterization of MLO Family Genes in Pumpkin (Cucurbita maxima Duch.)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khin%20Thanda%20Win">Khin Thanda Win</a>, <a href="https://publications.waset.org/abstracts/search?q=Chunying%20Zhang"> Chunying Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanghyeob%20Lee"> Sanghyeob Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mildew resistance locus o (Mlo), a plant-specific gene family with seven-transmembrane (TM), plays an important role in plant resistance to powdery mildew (PM). PM caused by Podosphaera xanthii is a widespread plant disease and probably represents the major fungal threat for many Cucurbits. The recent Cucurbita maxima genome sequence data provides an opportunity to identify and characterize the MLO gene family in this species. Total twenty genes (designated CmaMLO1 through CmaMLO20) have been identified by using an in silico cloning method with the MLO gene sequences of Cucumis sativus, Cucumis melo, Citrullus lanatus and Cucurbita pepo as probes. These CmaMLOs were evenly distributed on 15 chromosomes of 20 C. maxima chromosomes without any obvious clustering. Multiple sequence alignment showed that the common structural features of MLO gene family, such as TM domains, a calmodulin-binding domain and 30 important amino acid residues for MLO function, were well conserved. Phylogenetic analysis of the CmaMLO genes and other plant species reveals seven different clades (I through VII) and only clade IV is specific to monocots (rice, barley, and wheat). Phylogenetic and structural analyses provided preliminary evidence that five genes belonged to clade V could be the susceptibility genes which may play the importance role in PM resistance. This study is the first comprehensive report on MLO genes in C. maxima to our knowledge. These findings will facilitate the functional analysis of the MLOs related to PM susceptibility and are valuable resources for the development of disease resistance in pumpkin. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mildew%20resistance%20locus%20o%20%28Mlo%29" title="Mildew resistance locus o (Mlo)">Mildew resistance locus o (Mlo)</a>, <a href="https://publications.waset.org/abstracts/search?q=powdery%20mildew" title=" powdery mildew"> powdery mildew</a>, <a href="https://publications.waset.org/abstracts/search?q=phylogenetic%20relationship" title=" phylogenetic relationship"> phylogenetic relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=susceptibility%20genes" title=" susceptibility genes"> susceptibility genes</a> </p> <a href="https://publications.waset.org/abstracts/75919/genome-wide-identification-and-characterization-of-mlo-family-genes-in-pumpkin-cucurbita-maxima-duch" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75919.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">181</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9554</span> Polymorphism of HMW-GS in Collection of Wheat Genotypes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Ch%C5%88apek">M. Chňapek</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Tomka"> M. Tomka</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Peroutkov%C3%A1"> R. Peroutková</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20G%C3%A1lov%C3%A1"> Z. Gálová </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Processes of plant breeding, testing and licensing of new varieties, patent protection in seed production, relations in trade and protection of copyright are dependent on identification, differentiation and characterization of plant genotypes. Therefore, we focused our research on utilization of wheat storage proteins as genetic markers suitable not only for differentiation of individual genotypes, but also for identification and characterization of their considerable properties. We analyzed a collection of 102 genotypes of bread wheat (Triticum aestivum L.), 41 genotypes of spelt wheat (Triticum spelta L.), and 35 genotypes of durum wheat (Triticum durum Desf.), in this study. Our results show, that genotypes of bread wheat and durum wheat were homogenous and single line, but spelt wheat genotypes were heterogenous. We observed variability of HMW-GS composition according to environmental factors and level of breeding and predict technological quality on the basis of Glu-score calculation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genotype%20identification" title="genotype identification">genotype identification</a>, <a href="https://publications.waset.org/abstracts/search?q=HMW-GS" title=" HMW-GS"> HMW-GS</a>, <a href="https://publications.waset.org/abstracts/search?q=wheat%20quality" title=" wheat quality"> wheat quality</a>, <a href="https://publications.waset.org/abstracts/search?q=polymorphism" title=" polymorphism"> polymorphism</a> </p> <a href="https://publications.waset.org/abstracts/6533/polymorphism-of-hmw-gs-in-collection-of-wheat-genotypes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6533.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">463</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9553</span> Varietal Screening of Watermelon against Powdery Mildew Disease and Its Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asim%20Abbasi">Asim Abbasi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amer%20Habib"> Amer Habib</a>, <a href="https://publications.waset.org/abstracts/search?q=Sajid%20Hussain"> Sajid Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Sufyan"> Muhammad Sufyan</a>, <a href="https://publications.waset.org/abstracts/search?q=Iqra"> Iqra</a>, <a href="https://publications.waset.org/abstracts/search?q=Hasnain%20Sajjad"> Hasnain Sajjad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Except for few scattered cases, powdery mildew disease was not a big problem for watermelon in the past but with the outbreaks of its pathotypes, races 1W and 2W, this disease becomes a serious issue all around the globe. The severe outbreak of this disease also increased the rate of fungicide application for its proper management. Twelve varieties of watermelon were screened in Research Area of Department of Plant pathology, University of Agriculture, Faisalabad to check the incidence of powdery mildew disease. Disease inoculum was prepared and applied with the help of foliar spray method. Fungicides and plants extracts were also applied after the disease incidence. Percentage leaf surface area diseased was assessed visually with a modified Horsfall-Barratt scale. The results of the experiment revealed that among all varieties, WT2257 and Zcugma F1 were highly resistant showing less than 5% disease incidence while Anar Kali and Sugar baby were highly susceptible with disease incidence of more than 65%. Among botanicals neem extract gave best results with disease incidence of less than 20%. Besides neem, all other botanicals also gave significant control of powdery mildew disease than the untreated check. In case of fungicides, Gemstar showed least disease incidence i.e. < 10%, however besides control maximum disease incidence was observed in Curzate (> 30%). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=botanicals" title="botanicals">botanicals</a>, <a href="https://publications.waset.org/abstracts/search?q=fungicides" title=" fungicides"> fungicides</a>, <a href="https://publications.waset.org/abstracts/search?q=pathotypes" title=" pathotypes"> pathotypes</a>, <a href="https://publications.waset.org/abstracts/search?q=powdery%20mildew" title=" powdery mildew"> powdery mildew</a> </p> <a href="https://publications.waset.org/abstracts/79893/varietal-screening-of-watermelon-against-powdery-mildew-disease-and-its-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79893.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">297</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9552</span> Investigation of Clubroot Disease Occurrence under Chemical and Organic Soil Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zakirul%20Islam">Zakirul Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Yugo%20Kumokawa"> Yugo Kumokawa</a>, <a href="https://publications.waset.org/abstracts/search?q=Quoc%20Thinh%20Tran"> Quoc Thinh Tran</a>, <a href="https://publications.waset.org/abstracts/search?q=Motoki%20Kubo"> Motoki Kubo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clubroot is a disease of cruciferous plant caused by soil born pathogen Plasmodiophora brassicae and can significantly limit the production through rapid spreading. The present study was designed to investigate the effect of cultivation practices (chemical and organic soils) on clubroot disease development in Brassica rapa. Disease index and root bacterial composition were investigated for both chemical and organic soils. The bacterial biomass and diversity in organic soil were higher than those in chemical soil. Disease severity was distinct for two different cultivation methods. The number of endophytic bacteria decreased in the infected root for both soils. The increased number of endophytic bacterial number led to reduce the proliferation of pathogen spore inside the root and thus reduced the disease severity in organic plants. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clubroot%20disease" title="clubroot disease">clubroot disease</a>, <a href="https://publications.waset.org/abstracts/search?q=bacterial%20biomass" title=" bacterial biomass"> bacterial biomass</a>, <a href="https://publications.waset.org/abstracts/search?q=root%20infection" title=" root infection"> root infection</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20index" title=" disease index"> disease index</a>, <a href="https://publications.waset.org/abstracts/search?q=chemical%20cultivation" title=" chemical cultivation"> chemical cultivation</a>, <a href="https://publications.waset.org/abstracts/search?q=organic%20cultivation" title=" organic cultivation"> organic cultivation</a> </p> <a href="https://publications.waset.org/abstracts/169607/investigation-of-clubroot-disease-occurrence-under-chemical-and-organic-soil-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169607.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">81</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9551</span> Developing Rice Disease Analysis System on Mobile via iOS Operating System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rujijan%20Vichivanives">Rujijan Vichivanives</a>, <a href="https://publications.waset.org/abstracts/search?q=Kittiya%20Poonsilp"> Kittiya Poonsilp</a>, <a href="https://publications.waset.org/abstracts/search?q=Canasanan%20Wanavijit"> Canasanan Wanavijit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research aims to create mobile tools to analyze rice disease quickly and easily. The principle of object-oriented software engineering and objective-C language were used for software development methodology and the principle of decision tree technique was used for analysis method. Application users can select the features of rice disease or the color appears on the rice leaves for recognition analysis results on iOS mobile screen. After completing the software development, unit testing and integrating testing method were used to check for program validity. In addition, three plant experts and forty farmers have been assessed for usability and benefit of this system. The overall of users’ satisfaction was found in a good level, 57%. The plant experts give a comment on the addition of various disease symptoms in the database for more precise results of the analysis. For further research, it is suggested that image processing system should be developed as a tool that allows users search and analyze for rice diseases more convenient with great accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rice%20disease" title="rice disease">rice disease</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis%20system" title=" data analysis system"> data analysis system</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20application" title=" mobile application"> mobile application</a>, <a href="https://publications.waset.org/abstracts/search?q=iOS%20operating%20system" title=" iOS operating system"> iOS operating system</a> </p> <a href="https://publications.waset.org/abstracts/52616/developing-rice-disease-analysis-system-on-mobile-via-ios-operating-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52616.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">287</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9550</span> Fusarium Wilt of Tomato: Plant Growth, Physiology and Biological Disease Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amna%20Shoaib">Amna Shoaib</a>, <a href="https://publications.waset.org/abstracts/search?q=Sidrah%20Hanif"> Sidrah Hanif</a>, <a href="https://publications.waset.org/abstracts/search?q=Rashid%20Mehmood"> Rashid Mehmood</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Current research work was carried out to check influence of farmyard manure (FYM) in Lycopersicon esculentum L. against Fusarium oxysporum f. sp. lycopersici (FO) in copper polluted soil. Silt-loam soil naturally enriched with 70 ppm of Cu was inoculated with 1 x 106 spore suspensions of FO and incorporated with 0%, 1%, 1.5% or 2% FYM. The multilateral interaction of host-pathogen-metal-organic amendment was assessed in terms of morphology, growth, yield, physiology, biochemistry and metal uptake in tomato plant after 30 and 60 days of sowing. When soil was inoculated with FO, plant growth and biomass were significantly increased during vegetative stage, while declining during flowering stage with substantial increase in productivity over control. Infected plants exhibited late wilting and disease severity was found on 26-50% of plant during reproductive stage. Incorporation of up to 1% FYM suppressed disease severity, improved plant growth and biomass, while it decreased yield. Rest of manure doses was found ineffective in suppressing disease. Content of total chlorophyll, sugar and protein were significantly declined in FO inoculated plants and incorporation of FYM caused significant reduction or no influence on sugar and chlorophyll content, and no pronounced difference among different FYM doses were observed. On the other hand, proline, peroxidase, catalase and nitrate reductase activity were found to be increased in infected plants and incorporation of 1-2% FYM further enhanced the activity of these enzymes. Tomato plant uptake of 30-40% of copper naturally present in the soil and incorporation of 1-2% FYM markedly decreased plant uptake of metal by 15-30%, while increased Cu retention in soil. Present study concludes that lower dose (1%) of FYM could be used to manage disease, increase growth and biomass, while being ineffective for yield and productivity in Cu-polluted soil. Altered physiology/biochemistry of plant in response to any treatment could be served as basis for resistant against pathogen and metal homeostasis in plants. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lycopersicon%20esculentum" title="Lycopersicon esculentum">Lycopersicon esculentum</a>, <a href="https://publications.waset.org/abstracts/search?q=copper" title=" copper"> copper</a>, <a href="https://publications.waset.org/abstracts/search?q=Fusarium%20wilt" title=" Fusarium wilt"> Fusarium wilt</a>, <a href="https://publications.waset.org/abstracts/search?q=farm%20yard%20manure" title=" farm yard manure"> farm yard manure</a> </p> <a href="https://publications.waset.org/abstracts/11199/fusarium-wilt-of-tomato-plant-growth-physiology-and-biological-disease-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11199.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">414</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9549</span> An Organic Dye-Based Staining for Plant DNA</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Beg%C3%BCm%20Terzi">Begüm Terzi</a>, <a href="https://publications.waset.org/abstracts/search?q=%C3%96zlem%20Ate%C5%9F%20S%C3%B6nmezo%C4%9Flu"> Özlem Ateş Sönmezoğlu</a>, <a href="https://publications.waset.org/abstracts/search?q=Kerime%20%C3%96zkay"> Kerime Özkay</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmet%20Y%C4%B1ld%C4%B1r%C4%B1m"> Ahmet Yıldırım</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In plant biotechnology, electrophoresis is used to detect nucleic acids. Ethidium bromide (EtBr) is used as an intercalator dye to stain DNA in agarose gel electrophoresis, but this dye is mutagenic and carcinogenic. In this study, a visible, reliable and organic Ruthenium-based dye (N-719) for staining plant DNA in comparison to EtBr. When prestaining and post-staining for gel electrophoresis, N-719 stained both DNA and PCR product bands with the same clarity as EtBr. The organic dye N-719 stained DNA bands as sensitively and as clearly as EtBr. The organic dye was found to have staining activity suitable for the identification of DNA.Consequently, N-719 organic dye can be used to stain and visualize DNA during gel electrophoresis as alternatives to EtBr in plant biotechnology studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agarose%20gel" title="agarose gel">agarose gel</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA%20staining" title=" DNA staining"> DNA staining</a>, <a href="https://publications.waset.org/abstracts/search?q=organic%20dye" title=" organic dye"> organic dye</a>, <a href="https://publications.waset.org/abstracts/search?q=N-719" title=" N-719"> N-719</a> </p> <a href="https://publications.waset.org/abstracts/68758/an-organic-dye-based-staining-for-plant-dna" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68758.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">267</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9548</span> Wheat Dihaploid and Somaclonal Lines Screening for Resistance to P. nodorum</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lidia%20Kowalska">Lidia Kowalska</a>, <a href="https://publications.waset.org/abstracts/search?q=Edward%20Arseniuk"> Edward Arseniuk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Glume and leaf blotch is a disease of wheat caused by necrotrophic fungus Parastagonospora nodorum. It is a serious pathogen in many wheat-growing areas throughout the world. Use of resistant cultivars is the most effective and economical means to control the above-mentioned disease. Plant breeders and pathologists have worked intensively to incorporate resistance to the pathogen in new cultivars. Conventional methods of breeding for resistance can be supported by using the biotechnological ones, i.e., somatic embryogenesis and androgenesis. Therefore, an effort was undertaken to compare genetic variation in P. nodorum resistance among winter wheat somaclones, dihaploids and conventional varieties. For the purpose, a population of 16 somaclonal and 4 dihaploid wheat lines from six crosses were used to assess their resistance to P. nodorum under field conditions. Lines were grown in disease-free (fungicide protected) and inoculated micro plots in 2 replications of a split-plot design in a single environment. The plant leaves were inoculated with a mixture of P. nodorum isolates three times. Spore concentrations were adjusted to 4 x 10⁶ of viable spores per one milliliter. The disease severity was rated on a scale, where > 90% – susceptible, < 10% - resistant. Disease ratings of plant leaves showed statistically significant differences among all lines tested. Higher resistance to P. nodorum was observed more often on leaves of somaclonal lines than on dihaploid ones. On average, disease, severity reached 15% on leaves of somaclones and 30% on leaves of dihaploids. Some of the genotypes were showing low leaf infection, e.g. dihaploid D-33 (disease severity 4%) and a somaclone S-1 (disease severity 2%). The results from this study prove that dihaploid and somaclonal variation might be successfully used as an additional source of wheat resistance to the pathogen and it could be recommended to use in commercial breeding programs. The reported results prove that biotechnological methods may effectively be used in breeding for disease resistance of wheat to fungal necrotrophic pathogens. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=glume%20and%20leaf%20blotch" title="glume and leaf blotch">glume and leaf blotch</a>, <a href="https://publications.waset.org/abstracts/search?q=somaclonal" title=" somaclonal"> somaclonal</a>, <a href="https://publications.waset.org/abstracts/search?q=androgenic%20variation" title=" androgenic variation"> androgenic variation</a>, <a href="https://publications.waset.org/abstracts/search?q=wheat" title=" wheat"> wheat</a>, <a href="https://publications.waset.org/abstracts/search?q=resistance%20breeding" title=" resistance breeding"> resistance breeding</a> </p> <a href="https://publications.waset.org/abstracts/105612/wheat-dihaploid-and-somaclonal-lines-screening-for-resistance-to-p-nodorum" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105612.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">120</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9547</span> Micro/Nano-Sized Emulsions Exhibit Antifungal Activity against Cucumber Downy Mildew</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kai-Fen%20Tu">Kai-Fen Tu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jenn-Wen%20Huang"> Jenn-Wen Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yao-Tung%20%20Lin"> Yao-Tung Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cucumber is a major economic crop in the world. The global production of cucumber in 2017 was more than 71 million tonnes. Nonetheless, downy mildew, caused by Pseudoperonospora cubensis, is a devastating and common disease on cucumber in around 80 countries and causes severe economic losses. The long-term usage of fungicide also leads to the occurrence of fungicide resistance and decreases host resistance. In this study, six types of oil (neem oil, moringa oil, soybean oil, cinnamon oil, clove oil, and camellia oil) were selected to synthesize micro/nano-sized emulsions, and the disease control efficacy of micro/nano-sized emulsions were evaluated. Moreover, oil concentrations (0.125% - 1%) and droplet size of emulsion were studied. Results showed cinnamon-type emulsion had the best efficacy among these oils. The disease control efficacy of these emulsions increased as the oil concentration increased. Both disease incidence and disease severity were measured by detached leaf and pot experiment, respectively. For the droplet size effect, results showed that the 114 nm of droplet size synthesized by 0.25% cinnamon oil emulsion had the lowest disease incidence (6.67%) and lowest disease severity (33.33%). The release of zoospore was inhibited (5.33%), and the sporangia germination was damaged. These results suggest that cinnamon oil emulsion will be a valuable and environmentally friendly alternative to control cucumber downy mildew. The economic loss caused by plant disease could also be reduced. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=downy%20mildew" title="downy mildew">downy mildew</a>, <a href="https://publications.waset.org/abstracts/search?q=emulsion" title=" emulsion"> emulsion</a>, <a href="https://publications.waset.org/abstracts/search?q=oil%20droplet%20size" title=" oil droplet size"> oil droplet size</a>, <a href="https://publications.waset.org/abstracts/search?q=plant%20protectant" title=" plant protectant"> plant protectant</a> </p> <a href="https://publications.waset.org/abstracts/121602/micronano-sized-emulsions-exhibit-antifungal-activity-against-cucumber-downy-mildew" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121602.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">128</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9546</span> In vitro Antiviral Activity of Ocimum sanctum against Animal Viruses</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anjana%20Goel">Anjana Goel</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashok%20Kumar%20Bhatia"> Ashok Kumar Bhatia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ocimum sanctum, a well known medicinal plant is used for various alignments in Ayurvedic medicines. It was found to be effective in treating the humans suffering from different viral infections like chicken pox, small pox, measles and influenza. In addition, curative effect of the plant in malignant patients was also reported. In the present study, leaves of this plant were screened against animal viruses i.e. Bovine Herpes Virus-type-1 (BHV-1), Foot and Mouth disease virus (FMDV) and Newcastle Disease Virus (NDV). BHV-1 and FMDV were screened in MDBK and BHK cell lines respectively using cytopathic inhibition test. While NDV was propagated in chick embryo fibroblast culture and tested by haemagglutination inhibition test. Maximum non toxic dose of aqueous extract of Ocimum sanctum leaves was calculated by MTT assay in all the cell cultures and nontoxic doses were used for antiviral activity against viruses. 98.4% and 85.3% protection were recorded against NDV and BHV-1 respectively. However, Ocimum sanctum extract failed to show any inhibitory effect on the cytopathic effect caused by FMD virus. It can be concluded that Ocimum sanctum is a very effective remedy for curing viral infections in animals also. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bovine%20herpes%20virus-type-1" title="bovine herpes virus-type-1">bovine herpes virus-type-1</a>, <a href="https://publications.waset.org/abstracts/search?q=foot%20and%20mouth%20disease%20virus" title=" foot and mouth disease virus"> foot and mouth disease virus</a>, <a href="https://publications.waset.org/abstracts/search?q=newcastle%20disease%20virus" title=" newcastle disease virus"> newcastle disease virus</a>, <a href="https://publications.waset.org/abstracts/search?q=Ocimum%20sanctum" title=" Ocimum sanctum"> Ocimum sanctum</a> </p> <a href="https://publications.waset.org/abstracts/69775/in-vitro-antiviral-activity-of-ocimum-sanctum-against-animal-viruses" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69775.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">272</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9545</span> Optimizing Perennial Plants Image Classification by Fine-Tuning Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khairani%20Binti%20Supyan">Khairani Binti Supyan</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatimah%20Khalid"> Fatimah Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=Mas%20Rina%20Mustaffa"> Mas Rina Mustaffa</a>, <a href="https://publications.waset.org/abstracts/search?q=Azreen%20Bin%20Azman"> Azreen Bin Azman</a>, <a href="https://publications.waset.org/abstracts/search?q=Amirul%20Azuani%20Romle"> Amirul Azuani Romle</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Perennial plant classification plays a significant role in various agricultural and environmental applications, assisting in plant identification, disease detection, and biodiversity monitoring. Nevertheless, attaining high accuracy in perennial plant image classification remains challenging due to the complex variations in plant appearance, the diverse range of environmental conditions under which images are captured, and the inherent variability in image quality stemming from various factors such as lighting conditions, camera settings, and focus. This paper proposes an adaptation approach to optimize perennial plant image classification by fine-tuning the pre-trained DNNs model. This paper explores the efficacy of fine-tuning prevalent architectures, namely VGG16, ResNet50, and InceptionV3, leveraging transfer learning to tailor the models to the specific characteristics of perennial plant datasets. A subset of the MYLPHerbs dataset consisted of 6 perennial plant species of 13481 images under various environmental conditions that were used in the experiments. Different strategies for fine-tuning, including adjusting learning rates, training set sizes, data augmentation, and architectural modifications, were investigated. The experimental outcomes underscore the effectiveness of fine-tuning deep neural networks for perennial plant image classification, with ResNet50 showcasing the highest accuracy of 99.78%. Despite ResNet50's superior performance, both VGG16 and InceptionV3 achieved commendable accuracy of 99.67% and 99.37%, respectively. The overall outcomes reaffirm the robustness of the fine-tuning approach across different deep neural network architectures, offering insights into strategies for optimizing model performance in the domain of perennial plant image classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=perennial%20plants" title="perennial plants">perennial plants</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title=" image classification"> image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20networks" title=" deep neural networks"> deep neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=fine-tuning" title=" fine-tuning"> fine-tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=VGG16" title=" VGG16"> VGG16</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet50" title=" ResNet50"> ResNet50</a>, <a href="https://publications.waset.org/abstracts/search?q=InceptionV3" title=" InceptionV3"> InceptionV3</a> </p> <a href="https://publications.waset.org/abstracts/182850/optimizing-perennial-plants-image-classification-by-fine-tuning-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182850.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">66</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9544</span> Isolement and Identification of Major Constituents from Essential Oil of Launaea nudicaulis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Yakoubi">M. Yakoubi</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Belboukhari"> N. Belboukhari</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Cheriti"> A. Cheriti</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Sekoum"> K. Sekoum</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Launaea nudicaulis (L.) Hook.f. is a desert, spontaneous plant and endemic to northem Sahara, which belongs to the Asteraceae family. This species exists in the region of Bechar (Local name; El-Rghamma). In our knowledge, no work has been founded, except studies showing the antimicrobial and antifungal activity of methalonic extract of this plant. The present paper describes the chemical composition of the essential oil from Launaea nudicaulis and qualification of isolation and identification of some pure products by column chromatography. The essential oil from the aerial parts of Launaea nudicaulis (Asteraceae) was obtained by hydroditillation in 0.4% yield, led to isolation of four several new products. The isolation is made by column chromatography and followed by GC-IK and GC-MS analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Launaea%20nudicaulis" title="Launaea nudicaulis">Launaea nudicaulis</a>, <a href="https://publications.waset.org/abstracts/search?q=asteraceae" title=" asteraceae"> asteraceae</a>, <a href="https://publications.waset.org/abstracts/search?q=essential%20oil" title=" essential oil"> essential oil</a>, <a href="https://publications.waset.org/abstracts/search?q=column%20chromatography" title=" column chromatography"> column chromatography</a>, <a href="https://publications.waset.org/abstracts/search?q=GC-FID" title=" GC-FID"> GC-FID</a>, <a href="https://publications.waset.org/abstracts/search?q=GC-MS" title=" GC-MS"> GC-MS</a> </p> <a href="https://publications.waset.org/abstracts/10415/isolement-and-identification-of-major-constituents-from-essential-oil-of-launaea-nudicaulis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10415.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">301</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9543</span> Remote Sensing-Based Prediction of Asymptomatic Rice Blast Disease Using Hyperspectral Spectroradiometry and Spectral Sensitivity Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Selvaprakash%20Ramalingam">Selvaprakash Ramalingam</a>, <a href="https://publications.waset.org/abstracts/search?q=Rabi%20N.%20Sahoo"> Rabi N. Sahoo</a>, <a href="https://publications.waset.org/abstracts/search?q=Dharmendra%20Saraswat"> Dharmendra Saraswat</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Kumar"> A. Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajeev%20Ranjan"> Rajeev Ranjan</a>, <a href="https://publications.waset.org/abstracts/search?q=Joydeep%20Mukerjee"> Joydeep Mukerjee</a>, <a href="https://publications.waset.org/abstracts/search?q=Viswanathan%20Chinnasamy"> Viswanathan Chinnasamy</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20K.%20Chaturvedi"> K. K. Chaturvedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjeev%20Kumar"> Sanjeev Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rice is one of the most important staple food crops in the world. Among the various diseases that affect rice crops, rice blast is particularly significant, causing crop yield and economic losses. While the plant has defense mechanisms in place, such as chemical indicators (proteins, salicylic acid, jasmonic acid, ethylene, and azelaic acid) and resistance genes in certain varieties that can protect against diseases, susceptible varieties remain vulnerable to these fungal diseases. Early prediction of rice blast (RB) disease is crucial, but conventional techniques for early prediction are time-consuming and labor-intensive. Hyperspectral remote sensing techniques hold the potential to predict RB disease at its asymptomatic stage. In this study, we aimed to demonstrate the prediction of RB disease at the asymptomatic stage using non-imaging hyperspectral ASD spectroradiometer under controlled laboratory conditions. We applied statistical spectral discrimination theory to identify unknown spectra of M. Oryzae, the fungus responsible for rice blast disease. The infrared (IR) region was found to be significantly affected by RB disease. These changes may result in alterations in the absorption, reflection, or emission of infrared radiation by the affected plant tissues. Our research revealed that the protein spectrum in the IR region is impacted by RB disease. In our study, we identified strong correlations in the region (Amide group - I) around X 1064 nm and Y 1300 nm with the Lambda / Lambda derived spectra methods for protein detection. During the stages when the disease is developing, typically from day 3 to day 5, the plant's defense mechanisms are not as effective. This is especially true for the PB-1 variety of rice, which is highly susceptible to rice blast disease. Consequently, the proteins in the plant are adversely affected during this critical time. The spectral contour plot reveals the highly correlated spectral regions 1064 nm and Y 1300 nm associated with RB disease infection. Based on these spectral sensitivities, we developed new spectral disease indices for predicting different stages of disease emergence. The goal of this research is to lay the foundation for future UAV and satellite-based studies aimed at long-term monitoring of RB disease. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rice%20blast" title="rice blast">rice blast</a>, <a href="https://publications.waset.org/abstracts/search?q=asymptomatic%20stage" title=" asymptomatic stage"> asymptomatic stage</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20sensitivity" title=" spectral sensitivity"> spectral sensitivity</a>, <a href="https://publications.waset.org/abstracts/search?q=IR" title=" IR"> IR</a> </p> <a href="https://publications.waset.org/abstracts/175761/remote-sensing-based-prediction-of-asymptomatic-rice-blast-disease-using-hyperspectral-spectroradiometry-and-spectral-sensitivity-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175761.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">86</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=plant%20disease%20identification&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=plant%20disease%20identification&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=plant%20disease%20identification&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=plant%20disease%20identification&page=5">5</a></li> <li class="page-item"><a class="page-link" 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