International Journal of Science, Engineering and TechnologyAuthors: Mr. P. Loganathan, Aakash K V, Abinaya R, Hema M, Jeebika S H
Abstract: Rice is a primary food source for more than half of the global population. However, rice crops are highly susceptible to diseases such as Bacterial Leaf Blight (BLB), caused by BLB is caused by Xanthomonas oryzae pv. oryzae, which can reduce yields by up to 70% under severe conditions. Traditional detection methods rely on manual inspection and laboratory testing, which are time-consuming, costly, and require expert knowledge unavailable to most rural farmers. This paper proposes an Edge AI-based Convolutional Neural Network (CNN) model for detecting rice bacterial blight in real time. The system allows farmers to upload rice leaf images through a web interface, where a trained CNN model analyzes and predicts the disease instantly. The model is optimized using lightweight architectures such as MobileNetV2 and EfficientNet, making it suitable for edge devices with limited computational resources. The proposed system achieves up to 97.2% classification accuracy with sub-second inference on mobile hardware, significantly outperforming traditional approaches. It improves detection speed, accessibility, and farmer engagement, enabling timely preventive actions and meaningful reduction in crop loss.
