Abstract
Plant diseases could lead to huge production loss for the cultivators. These diseases are typically in the form of visible symptoms like color changes on the surface of the leaves, different colored spots, or streaks. This region of interest is extracted using image processing, and the area of the disease-affected part of the leaf is calculated. This system is proposed to support agriculturists to identify plant diseases efficiently and constantly monitor the health conditions of the plants. A convolutional neural network is used to identify common diseases of a few types of fruit leaves. The overall accuracy of this system is found to be 90% with a loss of 2.8%. Determining the disease and the leaf’s disease-affected area will help in maintaining a better quality of the crop by taking the required actions.
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Bhavani, M., Peeyush, K.P., Jayabarathi, R. (2023). Plant Health Analyzer Using Convolutional Neural Networks. In: Bindhu, V., Tavares, J.M.R.S., Vuppalapati, C. (eds) Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems . Lecture Notes in Electrical Engineering, vol 977. Springer, Singapore. https://doi.org/10.1007/978-981-19-7753-4_26
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DOI: https://doi.org/10.1007/978-981-19-7753-4_26
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