Integration of Support Vector Machine and Deep Convolutional Neural Network for Rice Crop Diseases Identification

Rice is one of the most important crops worldwide, and its production can be adversely affected by various diseases. Early detection and accurate identification of these diseases are essential for ensuring proper management of the crop. In this study, a hybrid model consisting of a Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM) was developed for the classification of nine different rice plant diseases. The DCNN was used for feature extraction, and the SVM was used for classification. The dataset used for training and testing the model consisted of images of rice plant diseases, with the DCNN achieving an accuracy of over 93% for all cases. The proposed hybrid model was then tested on a separate test dataset, achieving an accuracy of 97.5% with the DCNN-SVM combination. The results show the effectiveness of the proposed hybrid model for the classification of rice plant diseases.