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Horticulturae
Special Issues
Application of Intelligent Technology and Equipment in Horticultural Production
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Application of Intelligent Technology and Equipment in Horticultural Production

A special issue of Horticulturae (ISSN 2311-7524).

Deadline for manuscript submissions: 31 January 2026 | Viewed by 458

Special Issue Editors

Dr. Bin Li

E-Mail Website
Guest Editor
Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
Interests: intelligent agricultural equipment; facility environment control
Dr. Enli Lü

E-Mail Website
Guest Editor
College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: intelligent technology; smart agriculture; agricultural robots

Special Issue Information

Dear Colleagues,

Modern horticultural production faces unprecedented challenges, including the need to enhance efficiency, reduce resource consumption, and address labor shortages, while meeting the growing demand for sustainable, high-quality produce. Intelligent technologies and equipment—such as IoT sensors, robotics, artificial intelligence (AI), machine learning, and automated systems—are revolutionizing horticulture by enabling precision agriculture, optimizing resource use, and improving yield and product quality.

To harness the full potential of smart technologies in horticulture, interdisciplinary research is essential. This includes advancements in real-time monitoring, data-driven decision-making, the automation of cultivation and post-harvest processes, and the integration of these systems into diverse production environments. Furthermore, addressing challenges such as technology adoption barriers, cost-effectiveness, and scalability remains critical for widespread implementation.

This Special Issue aims to explore cutting-edge developments, challenges, and opportunities in the application of intelligent technologies and equipment across horticultural production systems. We welcome original research articles, reviews that bridge the gap between technological innovation, and practical horticultural applications.

Dr. Bin Li
Dr. Enli Lü
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

  • intelligent technology
  • horticultural production
  • precision agriculture
  • IoT sensors
  • artificial intelligence (AI)
  • robotics and automation
  • machine learning

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Published Papers (1 paper)

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Research

24 pages, 5644 KiB  
Article
Design and Optimization of Target Detection and 3D Localization Models for Intelligent Muskmelon Pollination Robots
by Huamin Zhao, Shengpeng Xu, Weiqi Yan, Defang Xu, Yongzhuo Zhang, Linjun Jiang, Yabo Zheng, Erkang Zeng and Rui Ren
Horticulturae 2025, 11(8), 905; https://doi.org/10.3390/horticulturae11080905 - 4 Aug 2025
Viewed by 349
Abstract
With the expansion of muskmelon cultivation, manual pollination is increasingly inadequate for sustaining industry development. Therefore, the development of automatic pollination robots holds significant importance in improving pollination efficiency and reducing labor dependency. Accurate flower detection and localization is a key technology for [...] Read more.
With the expansion of muskmelon cultivation, manual pollination is increasingly inadequate for sustaining industry development. Therefore, the development of automatic pollination robots holds significant importance in improving pollination efficiency and reducing labor dependency. Accurate flower detection and localization is a key technology for enabling automated pollination robots. In this study, the YOLO-MDL model was developed as an enhancement of YOLOv7 to achieve real-time detection and localization of muskmelon flowers. This approach adds a Coordinate Attention (CA) module to focus on relevant channel information and a Contextual Transformer (CoT) module to leverage contextual relationships among input tokens, enhancing the model’s visual representation. The pollination robot converts the 2D coordinates into 3D coordinates using a depth camera and conducts experiments on real-time detection and localization of muskmelon flowers in a greenhouse. The YOLO-MDL model was deployed in ROS to control a robotic arm for automatic pollination, verifying the accuracy of flower detection and measurement localization errors. The results indicate that the YOLO-MDL model enhances AP and F1 scores by 3.3% and 1.8%, respectively, compared to the original model. It achieves AP and F1 scores of 91.2% and 85.1%, demonstrating a clear advantage in accuracy over other models. In the localization experiments, smaller errors were revealed in all three directions. The RMSE values were 0.36 mm for the X-axis, 1.26 mm for the Y-axis, and 3.87 mm for the Z-axis. The YOLO-MDL model proposed in this study demonstrates strong performance in detecting and localizing muskmelon flowers. Based on this model, the robot can execute more precise automatic pollination and provide technical support for the future deployment of automatic pollination robots in muskmelon cultivation. Full article
(This article belongs to the Special Issue Application of Intelligent Technology and Equipment in Horticultural Production)
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