Hispa Rice Disease Classification using Convolutional Neural Network

The current work focuses on implementing a rice disease detection (RDD) system on hispa rice disease by using real-time rice plant images collected from rice fields of Punjab, trained on a CNN-based deep learning model. The dataset first gets preprocessed using a Matlab tool and then splits up into 70 to 30 ratio which further gets trained and validated on a proposed CNN model results in an accuracy of 94%. The motivation behind the proposed work is due to an unavailability of a system for RDD in case of hispa disease gave rise to a need for an efficient and trained system that will be useful for the detection of rice hispa disease.