Abstract:

Weed detection and removal are critical tasks in modern agriculture to maintain crop health and maximize yield.

Traditional methods are labor-intensive and inefficient, necessitating the need for automation.

This project presents a deep learning-based autonomous robotic system for weed detection and plucking using Raspberry Pi.

The system integrates a Pi camera for image acquisition, ultrasonic sensors for obstacle detection, motor drivers for robotic movement and arm control, and a 12V battery for power supply.

A convolutional neural network (CNN) is employed for real-time weed detection, enabling the robotic arm to pluck detected weeds with high precision.

The robot moves autonomously in a predefined direction, stopping when an obstacle is detected by the ultrasonic sensor.

An LCD display provides real-time feedback on the ongoing processes, such as weed detection, plucking, and obstacle avoidance.

The system demonstrates significant potential to reduce manual labor and increase the efficiency of weed management in agricultural fields.

Future enhancements could include model optimization for faster processing and integration of additional sensors for improved performance.

• Develop a Real-Time Weed Detection System: To design a deep learning-based weed detection system using a convolutional neural network (CNN) that can accurately identify and differentiate weeds from crops in real-time using a Pi camera.
• Integrate an Autonomous Robotic Platform: To implement an autonomous robot controlled by a Raspberry Pi, equipped with motor drivers and an obstacle-detecting ultrasonic sensor for continuous movement and navigation.
• Design a Robotic Arm for Weed Removal: To develop a motorized robotic arm capable of plucking weeds upon detection, controlled by a secondary motor driver for precise movement.
• Implement Obstacle Detection and Avoidance: To integrate ultrasonic sensors that allow the robot to detect obstacles in its path and stop or reroute to prevent collisions.
• Provide Real-Time Feedback via LCD Display: To display system status and feedback, including weed detection, plucking status, and obstacle alerts, on an LCD display for easy monitoring.
• Ensure Energy Efficiency and Mobility: To power the entire system using a 12V battery, ensuring sufficient energy for continuous operation of the robot, camera, motors, and sensors in a variety of agricultural environments.
• Test and Validate the System in Varied Conditions: To conduct extensive testing in different environmental conditions, ensuring reliable performance in detecting and removing weeds in both small and large agricultural fields.