Implementation of Convolutional Neural Network and Support Vector Machine Classification for Disease Detection in Rice Plants

Abstract

Rice is a major staple crop that is highly susceptible to various leaf diseases, necessitating an accurate early detection method to prevent yield losses. This study proposes a hybrid approach combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for rice leaf disease classification based on digital images. The CNN is employed as a deep feature extractor, while the SVM serves as the main classifier. The dataset consists of rice leaf images categorized into four disease types: bacterial blight, blast, brown spot, and tungro. The data were divided into training and validation sets, and the CNN model was trained for 10 epochs, achieving a validation accuracy of 98.14% at the 10th epoch. The extracted CNN features were then evaluated using different SVM kernels, namely Linear, Polynomial, RBF, and Sigmoid. The experimental results show that the Sigmoid kernel achieved the best performance with an accuracy of 49%, followed by Polynomial, RBF, and Linear kernels.