A Comparative Study of Two Convolutional Neural Network Models for Detecting Rice Plant Diseases Using Online and Local Image Data

Abstract

ABSTRACT

Rice is one of the most widely staple foods around the globe, however, rice fields are severely affected by diseases, which can disrupt global food security. Early and accurate detection of rice diseases is essential for the recovery of such rice plants. Manually identifying rice plant diseases can be tedious and error prone. Artificial intelligence (AI) driven models, such as Convolutional Neural Networks (CNN) have proven very successful in the identification or detection of various crop diseases. This study, therefore, presents a comparative study of the effectiveness of two popular CNN architectures; ResNet and AlexNet for the detection of rice plant disease. The data used to train the models include a combination of rice leaf images that were gathered locally from a rice field/farm in Ede, Osun State, Nigeria, and from an online repository. The dataset consists of 5200 images classified into four classes: Bacterial leaf blight, Brown spot, Blast, and Healthy, each containing 1300 images. The effectiveness of the two trained models was measured using classification performance metrics including Accuracy, Precision, Recall, and F1-Score. The finding from the study showed that The ResNet has a test accuracy of 95.25% as against 92.91% for the AlexNet. The ResNet had 0.93 precision, while AlexNet recorded a precision of 0.24. For recall, the ResNet model had 0.98 while the AlexNet model had 0.23 and for the f1-score, the ResNet model had 0.95 while the AlexNet model had 0.24. Generally, the ResNet model outperformed the AlexNet model in detecting rice plant diseases, most significantly, brown spot disease.