Abstract
More than 60% of people in India consume rice in their day-to-day life [1] hence it is essential to identify the diseases at an early stage to prevent them from causing further damage which increases the yield of rice. The automatic way of detecting and diagnosing rice diseases is highly required in the agricultural field. There are various models proposed by researchers which detect paddy disease. We have classified and listed models based on their architecture such as CNN (Convolutional Neural Network), ANN (Artificial Neural Network), and ML (Machine Learning). The best model out of these is selected by the following criteria mainly on their performance, efficiency, and the number of diseases a model can detect. Further, we have discussed image pre-processing and segmentation techniques used. This article will direct new researchers into this domain.
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Agilandeeswari, L., Kiruthik Suriyah, M. (2023). Comprehensive Analysis of Rice Leaf Disease Detection and Classification Models. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-35501-1_46
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DOI: https://doi.org/10.1007/978-3-031-35501-1_46
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