A Comparative Study on Paddy Disease Classification Using Machine Learning

In India, rice is grown in a diverse range of environmental conditions and is a significant source of energy and protein for people globally, accounting for 21% of per capita energy and 15% of per capita protein. India ranks second in terms of both area and production among the major rice-producing countries in the world. It produced 124.37 million tons of rice from 44.5 million hectares during the year 2020-2021, which is a massive portion of the 782 million tons of rice produced globally from 167.1 million hectares. Diseases that impact crop quality and yield are a problem for paddy farmers. Diseases that impact crop quality and yield are a problem for paddy farmers. Low production rates are caused by problems such as a lack of professional availability, ignorance about diseases and pests, and a lack of expertise in fertilizer management. By using captured images of infected leaves, farmers can quickly identify diseases and take appropriate action to prevent crop loss. Various image processing and machine learning algorithms have been developed to diagnose diseases in rice plants, and this study examines the different techniques used for feature extraction and classification in disease identification.