Precision agriculture has put in a lot of enhancement in improving agriculture in the last two decades. Plant monitoring is one of the essential applications of Precision Agriculture. In this study, an IoT-based system for rice leaf disease detection that runs on solar power and makes use of integrated machine learning on a Raspberry Pi 4 Model B is presented. In the classification of two important rice diseases, bacterial leaf blight and rice blast, the built custom Convolutional Neural Network (CNN) model, which was translated to TensorFlow Lite (TFLite) format for edge deployment, obtained a remarkable 94.28% accuracy. For scalable, effective disease detection in rice farming, this solarpowered, cost-effective device integrates edge AI and IoT.