Application of Artificial Intelligence and Simulation Technology in Fruits and Vegetables Production
A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Postharvest Biology, Quality, Safety, and Technology".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 61
Special Issue Editor
Interests: agricultural engineering; food engineering; mechanical equipment; intelligent technology; model; information perception; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Fruit and vegetable production, as a critical domain in modern agriculture, faces immense pressure to enhance efficiency, ensure quality, and address resource constraints and environmental changes. Traditional production models exhibit limitations in precision management, risk prediction, and resource optimization. However, the rapid advancement of artificial intelligence (AI) and simulation technologies offers unprecedented opportunities to tackle these challenges. These technologies enable in-depth analysis of complex crop growth environments, physiological processes, and traits, providing producers with intelligent decision-making support. This propels the industry toward precision, efficiency, and sustainability.
This Special Issue, "Application of Artificial Intelligence and Simulation Technology in Fruit and Vegetable Production", aims to compile and showcase cutting-edge research, innovative tools, advanced methodologies, and systemic solutions that successfully address key challenges in fruit and vegetable production using AI and simulation technologies. Key focus areas include, but are not limited to: machine learning and computer vision-based crop phenotyping analysis; intelligent pest and disease identification and early warning; yield prediction models; intelligent regulation and optimization of growth environments (light, temperature, water, air, nutrients); vision-guided navigation and operation of harvesting robots; non-destructive postharvest quality detection and grading; digital twin or system dynamics-based simulation and optimization of production processes; and virtual testing of harvesting protocols using simulation technology. We welcome innovative research and practices that leverage AI and simulation technologies to enhance the productivity, quality, resource utilization efficiency, and overall sustainability of fruit and vegetable production.
Dr. Changsu Xu
Guest Editor
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. Horticulturae 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 2200 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.
Keywords
- artificial intelligence
- machine vision
- maturity
- discrete element method
- finite element method
- harvesting robots
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