Deep Learning Based Weed Detection and Elimination in Agriculture

Abstract

Weed management is a crucial aspect of crop production that requires significant effort, time, and resources. Traditional weed management methods involve the use of herbicides and manual labor, which can be harmful to the environment and labor-intensive. Recent advances in computer vision and robotics have shown promising results in weed detection and elimination. This research study proposes a novel weed detection and elimination system using a combination of deep learning-based object detection and a robotic arm. The proposed system can detect and eliminate weeds autonomously, reducing the need for manual labor and herbicides. The system consists of a camera that captures images of the crops, which are processed by a Deep Neural Network (DNN) for weed detection. The detected weeds are then targeted and eliminated using a robotic arm equipped with a weed cutter. The proposed system was evaluated on a dataset of images collected from a maize field, and the results show that it can detect and eliminate weeds with an accuracy of over 80%. The proposed system has the potential to significantly reduce the labor and environmental impact of weed management, while also increasing the efficiency and accuracy of weed detection and elimination in crop production.