Enhanced VGG-19 model for rice plant disease detection and classification

Abstract

Rice is the main staple food and rice farming plays a crucial role in the agriculture sector of Myanmar. It is also an essential pillar in generating foreign income. However, rice diseases seriously reduced the rice production and quality. Early detection of rice diseases is one of the effective ways to reduce the disease spreading and increase yields. Most Myanmar farmers detect rice diseases based on visual judgment and their experience, which leads to delay in taking efficient action. To overcome this challenge, we intend to propose an enhanced rice plant disease classification model that contributes as artificial intelligence (AI) in Myanmar agriculture sector. The proposed model enhances original visual geometry group 19 (VGG-19) by integrating the algorithms: mixture of Gaussians 2 (MOG2), GrabCut, and relevance estimation with linear feature (RELIEF) for classification. It was trained on 6,326 rice plant images of Kaggle and Eastern Shan State and validated using 5-fold nested cross-validation. The training and testing of proposed model are followed as 80:20. The proposed model experimental result is (98.3%) and lowest standard deviation (0.004) across seven classes than the original VGG-19, MobileNet, Efficient Net, and RestNet50 respectively. Future work will expand dataset diversity, enhance early-stage disease prediction, and support mobile diagnostics for real-world agricultural application.