The population of the entire planet is growing every day. The main problem in the future years will be to feed everyone on the planet. Rice is one of the most significant crops since it feeds more than half of the world's population. Early disease detection is the main difficulty in rice crop cultivation. However, when productivity is affected, it might be challenging to diagnose sickness with the naked eye. The goal of this project is to increase production by more than 20% by the early diagnosis of Rice leaf disease. In order to identify and categorise illnesses including Stem borer, Sheath Blight Rot Brown Spot, and False Smut, this article presented a Convolution Neural Network (CNN) and deep learning technique. Because it can infect any leaf of any size, the leaf disease presents a significant diagnostic difficulty. To train the KNN model, a dataset of 1045 photos was acquired. KNN initially distinguishes between leaves with and without diseases. The classification of the disease will take conducted utilising CNN in the second phase. With this method, we were able to identify healthy leaves with 95% accuracy and Sheath Blight with 90% accuracy (the highest of all disorders).
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