Deep Learning-Based Rice Crop Disease Detection and Automated Pesticide Control
Kshitija Bhagat
Agriculture plays a vital role in ensuring food security, but crop diseases remain a major challenge that can significantly reduce yield and quality. Traditional disease detection methods rely on manual observation, which is time-consuming, error-prone, and requires expert knowledge. To address this issue, this project proposes a CNN-based Rice Crop Disease Detection and IoT-Enabled Smart Spraying System using Raspberry Pi.The system employs a Convolutional Neural Network (CNN) deep learning model trained on rice leaf images to accurately detect and classify common rice diseases such as Brown Spot, Leaf Blast, and Bacterial Leaf Blight. The trained model is integrated with a Raspberry Pi, enabling on-field deployment where farmers can upload or capture leaf images using a connected camera module. Once the model identifies the disease, the system automatically displays the result along with the recommended pesticide or biological treatment suitable for the detected condition.