EFFICIENT DIAGNOSIS OF RICE CROP DISEASE USING MACHINE LEARNING

Rice is a key staple crop, and productivity is highly impacted by numerous diseases. Conventional disease diagnosisapproaches are based on manual examination, which is cumbersome and subject to errors. The present work putsforward a framework for rice disease diagnosis using a machine learning technique based on convolutional NeuralNetworks (CNNs). The model was trained on a database of images of diseased and healthy rice leaves and reached84.8% classification accuracy. The system is developed to deployed as a web application to aid farmers in the earlydetection of disease and prescriptive treatment, hence enhancing crop yield. The use of cutting-edge technologies likemachine learning(ml) and deep learning (Dl) is capable of addressing these challenges through the early detection ofplant diseases.