Integrating AI and Robotics for Precision Weed Control in Agriculture

Dear Colleagues,

Weed control remains one of the most persistent and costly challenges in modern agriculture, particularly under the increasing pressure to reduce herbicide use, manage resistance and promote sustainability. Traditional methods—manual, mechanical, or chemical—are increasingly challenged by the need for environmental stewardship, labor shortages and the global imperative to secure food supply. The rising demand for sustainable and efficient crop production has catalyzed transformative changes within agricultural engineering, particularly in weed management. This Special Issue aims to address these pressing concerns by showcasing the latest advancements in precision weeding, leveraging cutting-edge technologies such as machine learning, robotics, sensor fusion and big data analytics.

Purpose:

The purpose of this Special Issue is to advance the science and technology of precision weeding, accelerating the transition from proof-of-concept trials to robust, scalable solutions in commercial agriculture. Our intent is to create a collaborative platform where researchers, technologists, and practitioners from diverse backgrounds can share breakthroughs, practical insights, and case studies.

Relationship to the existing literature:

While comprehensive reviews and research articles have examined specific facets of precision weeding such as machine vision methods or robotics, the landscape is rapidly evolving with the integration of AI, IoT, and big data. This issue seeks to bridge existing knowledge gaps by providing a unified, multidisciplinary perspective and highlighting real-world implementations, system integration challenges, and scalability. The collected works will both supplement the current literature by presenting novel methodologies and contextualize these within broader trends in sustainable agricultural technology.

Topics of Interest (but not limited to):

• Technological innovations, including computer vision, machine learning algorithms, real-time detection, and robotic systems;
• Field testing, hardware constraints, and climate variability;
• Data governance, AI ethics, adoption hurdles, and public trust;
• Analysing economic viability, environmental benefits, and relevant case studies;
• Cross-regional perspectives, comparing smallholder and industrial farming practices;
• Incorporating open-source toolkits and data sharing platforms could enhance collaboration and accelerate progress in agriculture;
• Technology of precision weeding;
• Machine learning, robotics, sensor fusion and big data analytics.

Dr. Asad (Md) Asaduzzaman
Dr. Quazi Mamun
Guest Editors

Manuscript Submission Information

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