Abstract
Plant leaf diseases have always been a matter of concern for farmers. With the increasing population, the demand for crops has increased which makes it very necessary and more important to not let the crops get damaged by viruses and bacteria. With the development of computer vision technologies and machine learning algorithms, it is easier now, than ever to use computers for the help of farmers in the early diagnosis of plant diseases and locating the infected portions of a plant. The existing computer vision models have seen considerable advancements in deep learning, the proposed approach is to integrate a few of them together to come up with an advanced model for classifying and localising the diagnosis of leaf diseases through coloured images. This paper proposes an algorithm ensembling a few state-of-the-art models in classification and object detection to build a model for Cassava leaf disease detection. The proposed ensemble model is found to achieve the categorization accuracy value of 90.75% on the Cassava leaf disease dataset by Makerere University.
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Kumar, H., Velu, S., Lokesh, A., Suman, K., Chebrolu, S. (2023). Cassava Leaf Disease Detection Using Ensembling of EfficientNet, SEResNeXt, ViT, DeIT and MobileNetV3 Models. In: Yadav, R.P., Nanda, S.J., Rana, P.S., Lim, MH. (eds) Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-8742-7_15
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