Applications of artificial intelligence in weed control: Brief overview on progresses and perspectives

Weed management remains a critical challenge in agriculture, directly affecting crop productivity and environmental sustainability. Traditional methods (i.e. mechanical, chemical, and cultural) are often labor-intensive, environmentally taxing, and limited by inefficiencies. Recent advancements in artificial intelligence (AI) offer promising solutions to address these issues through enhanced detection, mapping, and control strategies. This review summarizes current progress in applying AI techniques- including machine learning, deep learning, computer vision, and robotics-to weed control. It highlights key research developments, such as the deployment of convolutional neural networks for weed identification, Unmanned Aerial Vehicles (UAVs)-based spatial weed mapping, and autonomous robotic systems for targeted weed removal. While AI technologies offer promising advancements in weed control, several challenges remain, including data scarcity and quality, algorithm generalizability, infrastructure limitations, economic feasibility, and regulatory requirements. However, future directions emphasize integrating AI with Internet of Things (IoT) technologies, developing explainable models, and fostering multidisciplinary collaborations. Addressing these issues will be crucial for the widespread adoption of AI in weed management. Additionally, balancing technological integration with environmental sustainability and operational efficiency will be essential for the future success of AI-driven weed management systems.

کلیدواژه ها:

Autonomous robots ، Herbicide ، Machine learning ، Sustainable agriculture