Towards sustainable weed management: the role of artificial intelligence, sensing, and robotics

Weeds are a major constraint to crop production worldwide. Overreliance on herbicides has led to environmental concerns, selection of herbicide-resistant weed populations, and reduced agroecosystem resilience. Recent advances in artificial intelligence (AI), including machine learning and deep learning, along with innovations in remote sensing, robotics, and automation, are shifting weed management from uniform field-scale interventions to site-specific, plant-level decision-making. This review synthesizes current developments in AI-enabled precision weed management, with emphasis on sensing technologies, image-processing workflows, weed detection algorithms, and targeted weed-control systems. We also consider AI-assisted herbicide-response phenotyping, including crop injury, off-target drift, and herbicide-resistance assessment. We discuss commercial and emerging precision weed-management technologies, including targeted herbicide spraying, robotic mechanical weeding, and laser-based weed control. Beyond operational efficiency, we provide a perspective on how AI-driven precision technologies can support a paradigm shift toward agroecological weed management by enabling species-specific interventions that suppress highly competitive weeds while conserving ecologically facilitative or neutral plant communities. Collectively, these advances position AI-enabled precision weed management for reducing chemical inputs, diversifying selection pressure, enhancing agroecosystem resilience, and supporting sustainable agricultural intensification.