RICE LEAF DISEASES USING DEEP LEARNING WITH ARTIFICIAL INTELLIGENCE

Abstract

Agricultural productivity plays a crucial role in the Indian economy, making early detection of rice leaf diseases vital for sustaining crop health and yield. Rice plants are naturally susceptible to various leaf diseases, which, if not identified and managed in time, can significantly affect the quality and quantity of the produce. Automating the detection process helps reduce the extensive manual effort required to monitor large farmlands and enables early identification of disease symptoms as they appear on the leaves. This paper introduces an image segmentation-based algorithm using Neural Networks for the automatic detection and classification of rice leaf diseases. It also surveys various techniques that can be employed for effective identification, highlighting the importance of image segmentation in disease diagnosis.