Autonomous Agricultural Rover with AI Plant Disease Detection & Precision Spraying
IEEE Robotics Project. Raspberry Pi 4 vision-guided rover detecting foliar diseases via MobileNetV2 with GPS waypoint navigation.
Project Overview
Full-scale autonomous agricultural platform powered by Raspberry Pi 4, MobileNetV2 CNN classifier running on OpenCV/TensorFlow Lite, high-torque NEMA 17 stepper drive system, GPS neo-6m, and solenoid-valve relay spray system. ### Practical Student Engineering Solution Fully functional hardware demonstration prototype equipped with dedicated microcontrollers, precision sensors, actuators, and an interactive cloud/mobile telemetry dashboard. ### Key Learning Outcomes - Embedded C / MicroPython firmware architecture and sensor interfacing - IoT telemetry protocols (MQTT / HTTP / BLE / LoRaWAN) - Power regulation, hardware debugging, and PCB design principles - Comprehensive technical documentation aligned with IEEE academic standards
Modern commercial and industrial infrastructure requires automated, real-time sensing and telemetry. Conventional manual monitoring suffers from high latency, human error, and lack of predictive fault visibility for Autonomous Agricultural Rover with AI Plant Disease Detection & Precision Spraying.
A turnkey engineering prototype integrating high-precision sensor modules, dedicated microcontroller unit, and cloud telemetry protocols to automate measurement, trigger alert thresholds, and provide remote dashboard control.