Survey On Detecting Weed Plants In Fields Using AI Techniques

Abstract

Weed infestation is one of the major challenges in modern agriculture because weeds compete with crops for nutrients, sunlight, water, and space, thereby reducing crop productivity and quality. Traditional weed removal methods such as manual weeding and chemical herbicides are expensive, time-consuming, and harmful to the environment. Artificial Intelligence (AI) techniques provide an effective solution for automatic weed detection and classification in agricultural fields. This survey paper reviews various AI and Deep Learning (DL) techniques used for weed detection, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), YOLO, Deep Neural Networks (DNN), and image processing methods. The survey also discusses commonly used datasets, methodologies, accuracies, limitations, research gaps, and future research directions. The main objective of this survey is to provide a comprehensive understanding of AI-based weed detection systems and encourage further research in precision agriculture.