Artificial intelligence-driven weed management in precision agriculture: Advances, challenges, and future perspectives

Abstract

Weed control remains a persistent and resource-intensive challenge in modern agriculture, significantly impacting crop yields, resource use efficiency, and environmental sustainability. The advent of Artificial Intelligence (AI) technologies is revolutionizing weed management by introducing intelligent, data-driven solutions for weed detection, classification, mapping, and control. This review critically examines the current landscape and progress in applying AI techniques including machine learning, deep learning, computer vision, and robotics in site-specific weed management. It explores the integration of AI with remote sensing, unmanned aerial vehicles (UAVs), and Internet of Things (IoT) platforms to facilitate precise and sustainable weed control. Notable commercial applications, such as real-time precision spraying systems and autonomous weeding robots, are highlighted to demonstrate the practical relevance of AI in field conditions. Despite promising outcomes, challenges such as high data dependency, system complexity, and cost constraints hinder widespread adoption. This review highlights current research initiatives and explores future prospects, emphasizing how AI is reshaping weed management to be more efficient, sustainable, and cost-effective within the framework of precision agriculture.