Submit a Manuscript to the Journal
International Journal of Vegetable Science
For an Article Collection on
Data-Driven Decision Support Systems for Sustainable Vegetable Cultivation
Manuscript deadline
Article Collection Guest Advisor(s)
Alvaro Fuentes,
Jeonbuk National University, South Korea
[email protected]
Jiuqing Dong,
Shanghai Polytechnic University, China
[email protected]
Data-Driven Decision Support Systems for Sustainable Vegetable Cultivation
Vegetable production systems are undergoing a profound transformation driven by advances in data acquisition, artificial intelligence, and digital agriculture. From sensor-enabled greenhouses and field-based imaging to remote sensing and integrated farm management platforms, modern cultivation increasingly relies on data-driven decision support systems (DSS) to improve productivity, resource efficiency, and crop quality. These systems combine agronomic knowledge with computational intelligence to assist growers in monitoring crop growth, diagnosing plant health, optimizing environmental conditions, and managing risks across diverse production contexts. As vegetable supply chains face mounting pressures from climate variability, labor constraints, and sustainability goals, the development of robust and interpretable DSS has become a key research frontier. This Article Collection focuses on emerging methodologies, practical implementations, and interdisciplinary innovations that advance intelligent decision-making for sustainable vegetable cultivation.
Sustainable vegetable production requires balancing yield, environmental management, and economic viability in increasingly complex and uncertain conditions. Data-driven decision support systems offer the potential to transform traditional management practices by enabling real-time insights, predictive analytics, and adaptive responses to changing crop and environmental dynamics. By integrating multi-modal data streams, such as climate measurements, phenotypic observations, soil information, and operational records, these systems can support more precise irrigation, nutrient management, pest and disease monitoring, and harvest planning. However, challenges remain in scalability, interoperability, model transparency, and adoption across diverse production systems. Advancing research in this area is essential not only to improve efficiency but also to reduce inputs, minimize waste, and enhance resilience in vegetable farming.
Dr. Alvaro Fuentes is a Research Professor at Jeonbuk National University, Republic of Korea, and Research Team Leader of the Multimedia Laboratory at the Core Research Institute of Intelligent Robots. His research advances artificial intelligence, computer vision, and data-driven decision-support systems for sustainable agriculture, with a focus on plant phenotyping, crop monitoring, and intelligent cultivation technologies. He leads interdisciplinary initiatives that integrate agricultural science with advanced AI methodologies and collaborates with international partners across academia and industry. Dr. Fuentes is actively promoting responsible, trustworthy, and human-centered AI to support the future of sustainable, resilient agricultural systems.
Dr. Jiuqing Dong received his Ph.D. in Engineering from Jeonbuk National University, Republic of Korea, supported by the China Scholarship Council. His research focuses on computer vision and deep learning, with applications spanning plant disease recognition, behavior analysis, medical image segmentation, and out-of-distribution detection. He has authored publications in leading journals in artificial intelligence and image processing and has contributed to multiple research projects. Dr. Dong is a member of the Shanghai Computer Society and the Shanghai Computer Vision Committee, and regularly serves as a reviewer for international journals and conferences in agricultural engineering and artificial intelligence.
Disclosure Statement: Alvaro Fuentes and Jiuqing Dong declare no conflict of interest regarding this work.
Benefits of publishing open access within Taylor & Francis
Global marketing and publicity, ensuring your research reaches the people you want it to.
Article Collections bring together the latest research on hot topics from influential researchers across the globe.
Rigorous peer review for every open access article.
Rapid online publication allowing you to share your work quickly.
Submission Instructions
This Collection welcomes contributions that align with the journal’s focus on vegetable science while exploring computational, agronomic, and technological advances in decision support. Relevant topics include, but are not limited to, AI-assisted crop monitoring, precision horticulture, greenhouse and open-field sensing systems, phenotyping and imaging technologies, predictive modeling for growth and yield, digital twins for cultivation environments, and intelligence-based farm management. Studies addressing sustainability outcomes, resource optimization, climate adaptation, and knowledge-driven analytics are particularly encouraged. Both methodological innovations and application-oriented case studies are within scope, provided they demonstrate relevance to vegetable crops and production systems. The Collection welcomes Original Research Articles and Review Papers that provide novel insights, practical relevance, and interdisciplinary collaboration across horticulture, data science, and agricultural engineering.
All manuscripts submitted to this Article Collection will undergo desk assessment and peer-review as part of our standard editorial process.
Please review the journal scope and author submission instructions prior to submitting a manuscript.
The deadline for submitting manuscripts is August 31st, 2026.
To submit your papers to this Article Collection, please:
- Check "yes" for the question, "Are you submitting your paper for a specific special issue or article collection?"
- Select the relevant Article Collection from the drop-down menu under the question, "Special Issue or Article Collection Name."
All manuscripts submitted to this Article Collection will undergo desk assessment and peer-review as part of our standard editorial process. Guest Advisors for this Collection will not be involved in peer-reviewing manuscripts unless they are an existing member of the Editorial Board. Please review the journal Aims and Scope and author submission instructions prior to submitting a manuscript.