Rice Leaf Disease Detection for Precision Agriculture

Rice being crucial for sustaining over half of the global population, is recognized as one of the foremost plants in agriculture worldwide. The presence of diseases affecting rice plants can significantly influence both the quantity and quality of the harvest, sometimes resulting in crop losses ranging from 30 to 60%. This work focuses on automating the recognition and categorization of common rice leaf diseases using deep learning, particularly with respect to the DenseNet architecture. Preprocessing methods, the deployment of DenseNet for disease diagnosis, and the gathering of an extensive dataset are all part of this work. The efficacy of the model is demonstrated by the high recall, accuracy, and precision rates of the experimental data. This work presents the effectiveness of DenseNet, explores the implications of the results for precision agriculture, and identifies future directions for deep learning-based plant disease detection research