Publication

Abstract : Crop and weed detection is an important component of modern precision agriculture, which aims to maximize agricultural productivity while minimizing environmental effect. Traditional manual weed management and monitoring techniques are time-consuming, labor-intensive, and frequently lead to indiscriminate herbicide administration. This paper introduces a solution to this current challenge by developing a robot using the Raspberry Pi 4, web camera, L298N motor driver, 12 V Johnson motor, 6 V water pump, and the powerful YOLOv3 object detection algorithm. The robot’s key aim is to perform real-time crop monitoring, accurately detect weed species, and selectively apply herbicides to weed-infested areas. The web camera serves as the robot’s “eyes,” capturing high-resolution images of the agricultural field, while the YOLOv3 algorithm processes this visual data for precise crop and weed detection. By leveraging the advanced capabilities of the crop and weed detection robot, farmers can achieve higher crop yields, reduce chemical usage, and adopt a more sustainable approach to weed management.