Deep Learning Models Comparisons on Rice Image Disease Classification Techniques: VGG16, ResNet50, and InceptionV3

Accurately classifying plant diseases from images in agricultural disease detection is critical for timely and effective treatment. In this paper, a comparative analysis of three prominent deep learning models VGG16, ResNet50, and InceptionV3 applied to classify the rice disease by leaf of rice. A dataset of rice leaf images by Kaggle used, and each model’s performance was evaluated such as accuracy, precision, recall, and F1-score metrics. The strengths and weaknesses of each model are analyzed based on the results found and based on the findings, future work, and practical applications in the field of agricultural technology.