Automated Weed Detection and Removal using Deep Learning and Robotics

Abstract

Agricultural productivity is often reduced due to excessive weed growth, which competes with crops for essential resources such as sunlight, water, and nutrients. Traditional methods of weed control, including manual weeding and chemical herbicides, are labor-intensive, time-consuming, and environmentally harmful. This study introduces an automated weed detection and removal system integrating deep learning techniques with robotics to identify and eliminate weeds with high precision. The system employs convolutional neural networks (CNNs) for real-time image classification of crops versus weeds, combined with robotic actuators for precise mechanical weed removal. Experimental evaluation shows that this approach can achieve superior precision in contrast with conventional methods, reduce herbicide dependency, and improve agricultural sustainability.