Plant Disease Detection and Automated Spraying Robot: A CNN and IoT-Based Approach for Precision Agriculture

Agriculture is a vital sector that directly influences food security and economic stability, yet crop productivity is significantly affected by plant diseases. Traditional disease detection methods rely on manual observation, which is timeconsuming, subjective, and often inaccurate, especially over large agricultural fields. To overcome these limitations, this paper presents a Plant Disease Detection and Automated Spraying Robot that integrates image processing, deep learning, and robotic automation to enable precision agriculture. The system uses a smart camera to capture real-time images of plant leaves, which are analysed using a Convolutional Neural Network (CNN) trained to classify healthy and diseased leaves such as rust, powdery mildew, and viral infections. Based on the detection results, an ESP32-controlled mechanism automatically activates targeted pesticide spraying, ensuring accurate and timely treatment of infected plants. Obstacle detection and navigation are managed using ultrasonic sensors and motor drivers, enabling autonomous movement across the field. Cloud integration using Firebase allows real-time monitoring through a web dashboard that displays disease type and system status. Experimental results show an average detection confidence of 76-85% and a spray-activation accuracy of 100% when disease confidence exceeds the 60% threshold, with a total system response time of under 5 seconds. The proposed system reduces manual labour, minimizes pesticide wastage, limits farmer exposure to harmful chemicals, and improves crop yield.