Classification of Rice Leaf Diseases and Pests Using the ResNet50 Model on the AgroGuard AI Dataset

Abstract

Rice leaf diseases and pests are one of the main factors causing decreased rice productivity. Manual disease identification still relies on the experience of farmers and extension workers, potentially leading to delayed diagnosis and mishandling. This study aims to develop an image-based rice leaf disease and pest classification model using the ResNet50 deep learning architecture. The dataset used comes from AgroGuard AI and consists of seven classes: blast disease, healthy leaves, insect attacks, leaf roller pests, leaf scald disease, brown spot disease, and tungro disease. The dataset is divided into training, validation, and test data with a ratio of 70%:15%:15%, where the test data is balanced with 400 images in each class. The ResNet50 model was trained from scratch without pre-training weights with a batch size of 32, a learning rate of 0.001, and 50 epochs. The evaluation results showed that the model achieved an accuracy of 77.86% on the test data, with a training accuracy of 80.52% and a validation accuracy of 89.38%. Evaluation using a confusion matrix and precision, recall, and F1-score metrics indicated that the model performed quite well and stably across all classes.