Abstract
Plant leaf diseases have always been a matter of concern for the farmers. With increasing population, the demand for crops has increased which makes it very necessary and more important to not let the crops get damaged from viruses and bacteria. With development in computer vision technologies and machine learning algorithms, it is easier now than ever to use the computers for the help of farmers in early detection of diseases in plants and locating the infected portions of plant. Considerable advancements in deep learning have been seen in the existing computer vision models present, and a proposed approach to addition of a couple of them together to come up with a most advanced model for categorizing and localizing the detection of leaf diseases through colored images has been put forward. This paper proposes an algorithm, ensembling a few models developed in object recognition and classification to build a model for disease detection in cassava leaf. The designed ensemble model is found to achieve the classification accuracy value of 90.68% experimented on cassava leaf disease dataset crated by Makerere University.
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Ruthvik Chowdary, C., Krishna, M., Rani, B.S.B.P., Hanuman Narendra, G., Satyanarayana, G. (2023). Disease Detection in Cassava Leaf Using Ensembling of EfficientNet, ResNext, ViT, DeIT and MobNetV3. In: Reddy, V.S., Prasad, V.K., Wang, J., Rao Dasari, N.M. (eds) Intelligent Systems and Sustainable Computing. ICISSC 2022. Smart Innovation, Systems and Technologies, vol 363. Springer, Singapore. https://doi.org/10.1007/978-981-99-4717-1_41
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DOI: https://doi.org/10.1007/978-981-99-4717-1_41
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