Application of deep learning for rice leaf disease detection in the Mekong Delta - Duc Luu Ngo; Thi Thuy Diem Le; Thi Phuong Anh Ha (CTU Journal of Innovation and Sustainable Development)
The Mekong River Delta, the largest rice-producing region in Vietnam with an annual output of over 25 million tons, plays a vital role in ensuring food security both within the country and globally. In recent years, it has undergone significant transformation in rice cultivation, which aims to support farmers here to plant rice more effectively. However, severe weather conditions and soil degradation have negatively impacted rice growth. Additionally, rice is highly susceptible to various diseases that must be identified and prevented promptly. As a result, leveraging technology such as AI and deep learning to diagnose rice diseases based on leaf symptoms is essential. This paper utilizes an image dataset of three common rice leaf diseases—leaf smut, brown spot, and bacterial leaf blight—and applies deep learning networks (MobileNet and ResNet) to evaluate and select the best model. A diagnostic program is then developed to detect these diseases. Experimental results show that the MobileNetV3-Small model (a variant of the MobileNet network) is the most optimal, offering fast training time, high accuracy, and acceptable levels of loss and error.