In this project, a solar-powered smart pesticide sprayer is developed to enable sustainable and intelligent agricultural practices by integrating renewable energy, artificial intelligence, and IoT technologies. The system utilizes a solar panel with a battery storage unit to provide a reliable and eco-friendly power source, making it suitable for operation in remote and off-grid agricultural fields.A Raspberry Pi 4 serves as the central processing unit, executing a YOLOv8-based deep learning model for real-time pest detection using images captured through a USB camera. Based on the detection results, the system performs selective pesticide spraying, activating the spray mechanism only in infected areas. This targeted approach significantly reduces excessive pesticide usage, minimizes environmental impact, and enhances crop protection efficiency.An ultrasonic sensor is incorporated to continuously monitor the pesticide tank level, ensuring efficient resource management and preventing dry operation of the pump. The spraying mechanism is controlled through a relay module connected to a DC pump, enabling automated and precise operation. For communication and remote monitoring, an ESP32 microcontroller is integrated to transmit real-time data to a mobile application via Wi-Fi. The application provides users with live updates on key parameters such as tank level, battery status, spray activity, and system performance, enabling effective monitoring and decision-maki
Tools: Raspberry Pi 4, ESP32 Microcontroller,Solar Panel, Charge Controller,YOLOv8 Object Detection Model
Department: Department of Electrical Engineering
Poster
