Wireless Surveillance Robotic Car for Crop Disease Detection

This project addresses the challenges of manual crop disease monitoring by introducing a smart robot system. Using advanced image processing and Convolution Neural Network (CNN), the system swiftly identifies diseases to aid farmers in efficient management. The robot, designed for large fields, employs real-time data processing and path finding algorithms for optimal coverage. Key technologies include precise GPS navigation and SLAM for virtual mapping, guiding the robot strategically. Equipped with sensors for disease identification, the project emphasizes user-friendly design, displaying affected areas on a digital map. Beyond disease detection, the initiative promotes sustainability by reducing pesticide usage, optimizing crop management, and enhancing productivity. Through the integration of robotics, AI, and data analytics, the project envisions advancing crop health and fostering sustainable farming practices. The step-by-step process involves GPS field selection after creating a virtual map using the LIDAR-SLAM algorithm, ensuring precise navigation, disease detection, mapping, and effective communication. Driven by personal experience, technology, and a forward-looking vision, the project aims to create a resilient, productive, and sustainable agricultural landscape, leveraging GPS and LIDAR-SLAM for efficient disease monitoring.