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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AgriEngineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• precision weeding
• machine learning in agriculture
• next generation weed detection
• agricultural robotics
• sensor fusion
• site-specific weed management
• autonomous weeding
• precision agriculture
• smart farming