Durian is a high-value tropical fruit widely cultivated in Southeast Asia, particularly in Vietnam, Thailand, Malaysia, and Indonesia. However, durian trees are vulnerable to numerous leaf diseases that can significantly reduce yield and fruit quality. In this study, we propose an artificial intelligence-based approach using the VGG16 convolutional neural network to classify diseases on durian leaves. A dataset of 5,603 images representing 10 common diseases and 1 non-disease class was collected and augmented to enhance model generalization. The model achieved a classification accuracy of 94.13% and a loss value of 0.5419, demonstrating its effectiveness compared to ResNet-50. Furthermore, a web-based application was developed to allow farmers to upload leaf images for instant disease diagnosis and receive treatment recommendations. This approach provides a practical, accessible tool to support early disease detection and precision agriculture, contributing to improved productivity and sustainable farming practices.
