Deep Learning-Based Paddy Disease Identification Using Light Weight CNN

A number of countries rely heavily on agriculture for both survival and economic growth. Among the many food crops, rice is one of the most significant when it comes to disease impact at different stages. A multitude of illnesses lead to issues with crop development and quality. Early detection of some diseases can be challenging, making normal procedures or the use of the unaided eye inadequate due to the similarity of symptoms. On the other hand, automation is very helpful in early crop loss prevention by identifying illnesses at the right time in crop production. Researchers employ a variety of techniques, including visual recognition, deep neural networks, and machine Intelligence, to create these automated processes. There are comparisons based on accuracy, methodology, or datasets in the literature on a range of illnesses that affect rice crops. The study is broken down into two primary sections: the examination of the survey methodology and the use of CNN's cutting-edge illness detection technology.