Artificial Intelligence and Machine Vision for Full-Cycle Orchard Production Management and Harvest
A special issue of Horticulturae (ISSN 2311-7524).
Deadline for manuscript submissions: 28 February 2026 | Viewed by 163
Special Issue Editors
Interests: artificial intelligence; computer vision; smart orchard; fruit detection and segmentation; agricultural information technology and equipment
Special Issues, Collections and Topics in MDPI journals
Interests: fruit germplasm resource; molecular breeding
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Efficient management across the full production cycle of fruit orchards from early phenological monitoring to harvest is essential for achieving high yields, ensuring fruit quality, and maintaining long-term orchard sustainability. Each stage including flowering, fruit setting, growth, ripening, and harvest presents unique challenges that require timely and precise decision making in areas such as pollination, fertilization, thinning, pest and disease control, and harvesting operations.
Traditional manual observations and interventions are often labor-intensive, subjective, and inefficient for large-scale orchards. With the rapid advancement of artificial intelligence (AI), especially in computer vision (CV) and intelligent sensing, automated solutions have emerged as powerful tools to address these challenges. These technologies enable the accurate monitoring of phenological changes, the detection of fruits at different growth stages, yield estimation, and the optimization of harvest timing and logistics, thereby enhancing productivity and reducing resource waste.
This Special Issue aims to bring together cutting-edge research on AI and machine vision for orchard production management across the entire cultivation cycle. Contributions addressing, but not limited to, the following topics are welcome:
- Flower detection and blooming stage recognition;
- Phenological phase monitoring throughout the growth cycle;
- Small and mature fruit detection, segmentation, and classification;
- Fruit growth tracking and yield estimation;
- Intelligent pest, disease, and nutrient monitoring systems;
- Harvest maturity assessment and optimal harvest scheduling;
- Autonomous and semi-autonomous harvesting technologies;
- Development of orchard-specific annotated datasets;
- Deep learning models for full-cycle phenotypic analysis;
- Integration of AI with IoT and robotics for orchard management.
By showcasing recent innovations in AI and CV for full-cycle orchard management and harvesting, this Special Issue aims to foster interdisciplinary collaboration among researchers in horticulture, agricultural engineering, plant phenotyping, robotics, and intelligent sensing technologies.
Dr. Weikuan Jia
Dr. Nan Wang
Dr. Danyan Chen
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. 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
- computer vision
- full-cycle orchard management
- fruit orchards
- phenological phases
- harvest
- flower detection
- fruit detection and segmentation
- pest and disease control
- yield estimation
- autonomous harvesting
- deep learning models
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