Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.1
3.0
Journals
Horticulturae
Special Issues
Application of Artificial Intelligence and Simulation Technology in Fruits and...
share announcement

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: 30 November 2026 | Viewed by 2023

Special Issue Editor

Dr. Changsu Xu

E-Mail Website
Guest Editor
College of Engineering and Technology, Southwest University, Chongqing 400715, China
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 250 words) can be sent to the Editorial Office for assessment.

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 7207 KB  
Article
Visual Understanding of Intelligent Apple Picking: Detection-Segmentation Joint Architecture Based on Improved YOLOv11
by Bin Yan and Qianru Wu
Horticulturae 2026, 12(4), 494; https://doi.org/10.3390/horticulturae12040494 - 18 Apr 2026
Viewed by 1022
Abstract
Achieving precise fruit localization and fine branch segmentation simultaneously in unstructured orchard environments remains challenging due to variable lighting, occlusion, and complex backgrounds. This study proposed a joint detection–segmentation architecture based on an improved YOLOv11 network for collaborative perception of apples and tree [...] Read more.
Achieving precise fruit localization and fine branch segmentation simultaneously in unstructured orchard environments remains challenging due to variable lighting, occlusion, and complex backgrounds. This study proposed a joint detection–segmentation architecture based on an improved YOLOv11 network for collaborative perception of apples and tree branches. First, a dual-task dataset of spindle-type apple orchards was constructed with bounding-box annotations for fruits and pixel-level polygon masks for branches, encompassing diverse illumination and occlusion conditions. Second, Convolutional Block Attention Modules (CBAMs) are strategically embedded into the YOLOv11 backbone to enhance feature discrimination for slender branch structures while preserving high fruit detection accuracy. The enhanced model achieves precision of 0.981, recall of 0.986, and F1-score of 0.983 for apple detection, and precision of 0.803, recall of 0.715, mAP of 0.698, and IoU of 0.6066 for branch segmentation on the validation set. Comparative experiments against YOLOv8 and baseline YOLOv11 confirm improved segmentation continuity and finer branch delineation. The proposed integrated perception framework provides reliable visual guidance for collision-avoidance robotic harvesting and offers a practical reference for multi-task agricultural vision systems. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Simulation Technology in Fruits and Vegetables Production)
►▼ Show Figures

Figure 1

23 pages, 7583 KB  
Article
Attention–Diffusion–Fusion Paradigm for Fine-Grained Lentinula edodes Maturity Detection
by Xingmei Xu, Jiali Wang, Zhanchen Wei, Shujuan Wei and Jinying Li
Horticulturae 2026, 12(1), 76; https://doi.org/10.3390/horticulturae12010076 - 8 Jan 2026
Viewed by 569
Abstract
The maturity of Lentinus edodes directly affects its quality, taste, and market value. Currently, maturity assessment primarily relies on manual experience, making it difficult to ensure efficiency and consistency. To achieve efficient and accurate detection of Lentinus edodes maturity, this study proposes an [...] Read more.
The maturity of Lentinus edodes directly affects its quality, taste, and market value. Currently, maturity assessment primarily relies on manual experience, making it difficult to ensure efficiency and consistency. To achieve efficient and accurate detection of Lentinus edodes maturity, this study proposes an improved lightweight object detection model, YOLOv8n-CFS. Based on YOLOv8n, the model integrates the SegNeXt Attention structure to enhance key feature extraction capabilities and optimize feature representation. A Feature Diffusion Propagation Network (FDPN) is designed to improve the expressive ability of objects at different scales through cross-layer feature propagation, enabling precise detection. The CSFCN module combines global cue reasoning with fine-grained spatial information to enhance detection robustness and generalization performance in complex environments. The CWD method is adopted to further optimize the model. Experimental results demonstrate that the proposed model achieves 97.34% mAP50 and 84.5% mAP95 on the Lentinus edodes maturity detection task, representing improvements of 2.02% and 4.92% compared to the baseline method, respectively. It exhibits excellent stability in five-fold cross-validation and outperforms models such as Faster R-CNN, YOLOv5n, YOLOv7-tiny, YOLOv8n, YOLOv8s, YOLOv10n, YOLOv11n, and YOLOv12. This study provides efficient and reliable technical support for Lentinus edodes maturity detection and holds significant implications for the intelligent production of edible fungi. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Simulation Technology in Fruits and Vegetables Production)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop