Call for papers - AI-powered plant monitoring

Guest Editors

Alvaro Fuentes, PhD, Jeonbuk National University, South Korea
Adel Hafiane, PhD, INSA CVL-University of Orléans, France

Submission Status: Open | Submission Deadline: 29 December 2025

BMC Artificial Intelligence is calling for submissions to our Collection on AI-powered plant monitoring. We aim to showcase research that explores applications in plant trait assessment, crop yield prediction, disease detection, and overall improvements in agricultural practices.

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Meet the Guest Editors

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Alvaro Fuentes, PhD, Jeonbuk National University, South Korea

Alvaro Fuentes, PhD, is a Research Professor at the Core Research Institute of Intelligent Robots and the Department of Electronic Engineering at Jeonbuk National University, South Korea, where he also serves as the Research Team Leader of the Multimedia Laboratory. His research focuses on artificial intelligence, computer vision, and robotics. He is also a fellow at the Trustworthy AI Lab at Seoul National University. Dr Fuentes earned his PhD and master’s degrees in Electronic Engineering with a specialization in Artificial Intelligence and Computer Vision from Jeonbuk National University. Throughout his research career in South Korea, he has contributed to high-impact AI research, particularly in the field of AI in agriculture. Additionally, he holds a degree in Mechatronics Engineering from the Technical University of the North in Ecuador. He has also been a DAAD fellow in Germany, participating in research and academic exchanges. Dr Fuentes has authored high-impact publications and serves as an editor and reviewer for international academic journals. His research and teaching interests include Computer Vision, Deep Learning, Machine Learning, and Robotics, with a strong commitment to Sustainable Development and Trustworthy Technologies, particularly in advancing technology responsibly and ethically for the benefit of society.

Adel Hafiane, PhD, INSA CVL-University of Orléans, France

Dr Adel Hafiane is an associate professor at the Institut National des Sciences Appliquées (INSA CVL), France, where he heads the Image and Vision group at PRISME Laboratory. He received the MS degree in embedded systems and information processing and the PhD degree from the University of Paris-Saclay in 2002 and 2005, respectively. He has been an invited researcher in the computer science department of the University of Missouri where he was previously a postdoctoral fellow. His research interests include the theory and methods of machine learning and computer vision for a wide range of applications. He has coordinated several research projects and co-authored more than 90 papers and 3 patents. He has also served as an associate editor for several special issues focused on deep learning applications in image analysis.

About the Collection

BMC Artificial Intelligence is calling for submissions to our Collection on AI-powered plant monitoring.

Automated monitoring and phenotyping in plant biology is a rapidly growing field combining artificial intelligence (AI) and traditional plant science. This approach enables the high-throughput assessment of plant growth, plant traits, and health status, allowing researchers to analyze complex data sets for factors that are crucial to understanding plant development and response to the environment. This facilitates not only the evaluation of morphological and physiological traits but also the detection of plant stressors and diseases, significantly advancing our knowledge of plant biology.

The significance of this automated monitoring and phenotyping lies in its potential to revolutionize agricultural practices. Recent technological advancements have led to improved methods for monitoring crop yield, plant health, and overall performance in various conditions. By harnessing the power of AI and deep learning algorithms, researchers can uncover patterns and insights that traditional methods may overlook. This has the potential to accelerate the breeding process and also increase the resilience of crops to climate change and various stressors, ultimately contributing to food security.

If this line of research continues to evolve, we may witness some significant advances in the way we approach agricultural challenges. These may include the development of highly specialized algorithms that tailor monitoring and phenotyping methodologies to specific plant species or environmental conditions. Improvements in automation and real-time data analytics could pave the way for precise crop management systems, leading to smarter agricultural practices and optimized resource use. This collection aims to publish new research methodologies and findings in the field of plant monitoring and phenotyping, with a focus on the integration of artificial intelligence (AI) to enhance precision, efficiency, and scalability. We welcome contributions on a broad spectrum of topics related to AI applications in plant monitoring and phenotyping, including but not limited to:

AI applications in plant trait analysis and phenotyping
Crop breeding strategies using AI
AI-driven approaches for plant growth assessment
Automated systems for plant disease detection
AI based methods for yield analysis and prediction

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

Please email Alison Cuff, the editor for BMC Artificial Intelligence, (alison.cuff@biomedcentral.com) if you would like more information before you submit.

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "AI-powered plant monitoring" from the dropdown menu.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.