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Horticulturae
Special Issues
Application of Smart Technology and Equipment in Horticulture—2nd Edition
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Application of Smart Technology and Equipment in Horticulture—2nd Edition

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2259

Special Issue Editors

Dr. Chenglin Wang

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Guest Editor
Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Interests: agricultural Internet of Things; robot vision; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Lufeng Luo

E-Mail Website
Guest Editor
School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China
Interests: intelligent perception; control of robots; agricultural robots; intelligent production lines; digital twins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the tremendous success of the first edition of the Special Issue “Application of Smart Technology and Equipment in Horticulture” (https://www.mdpi.com/journal/horticulturae/special_issues/R87IUNW023), a second edition is being launched.

As an important aspect of modern agriculture, horticulture also plays a crucial role in beautifying the environment and enriching human nutrition. Now, with the application of intelligent devices in all aspects of agriculture, horticulture—an agricultural form that requires more refined management and operation—has begun to pursue intelligence and intensification. Therefore, the demand for advanced gardening technology and intelligent equipment is growing.

In order to develop intelligent technology and equipment that can aid in gardening, beautify the environment, and support the cultivation and breeding of plants, research is needed to improve the popularity of intelligent equipment and the survival rate of breeding. Successful breeding can enrich our choices, and automated gardening can accelerate urban greening. Similarly, intelligent technology and equipment in intensive horticulture can not only reduce the cost of manpower, but also enhance the accuracy and efficiency of management, thus increasing the output.

This Special Issue focuses on the current intelligent technology and equipment utilized to beautify the environment, promote agricultural intensification, and cultivate and breed species of plants. We invite researchers to submit articles to this Special Issue and present their own views and opinions. We will support all researchers in this regard.

Dr. Chenglin Wang
Dr. Lufeng Luo
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

  • horticultural intelligent equipment
  • horticultural artificial intelligence technology
  • modern agricultural technology

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Published Papers (2 papers)

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12 pages, 7796 KiB  
Article
A Multi-Fruit Recognition Method for a Fruit-Harvesting Robot Using MSA-Net and Hough Transform Elliptical Detection Compensation
by Shengxue Wang and Tianhong Luo
Horticulturae 2024, 10(10), 1024; https://doi.org/10.3390/horticulturae10101024 - 26 Sep 2024
Viewed by 753
Abstract
In the context of agricultural modernization and intelligentization, automated fruit recognition is of significance for improving harvest efficiency and reducing labor costs. The variety of fruits commonly planted in orchards and the fluctuations in market prices require farmers to adjust the types of [...] Read more.
In the context of agricultural modernization and intelligentization, automated fruit recognition is of significance for improving harvest efficiency and reducing labor costs. The variety of fruits commonly planted in orchards and the fluctuations in market prices require farmers to adjust the types of crops they plant flexibly. However, the differences in size, shape, and color among different types of fruits make fruit recognition quite challenging. If each type of fruit requires a separate visual model, it becomes time-consuming and labor intensive to train and deploy these models, as well as increasing system complexity and maintenance costs. Therefore, developing a general visual model capable of recognizing multiple types of fruits has great application potential. Existing multi-fruit recognition methods mainly include traditional image processing techniques and deep learning models. Traditional methods perform poorly in dealing with complex backgrounds and diverse fruit morphologies, while current deep learning models may struggle to effectively capture and recognize targets of different scales. To address these challenges, this paper proposes a general fruit recognition model based on the Multi-Scale Attention Network (MSA-Net) and a Hough Transform localization compensation mechanism. By generating multi-scale feature maps through a multi-scale attention mechanism, the model enhances feature learning for fruits of different sizes. In addition, the Hough Transform ellipse detection compensation mechanism uses the shape features of fruits and combines them with MSA-Net recognition results to correct the initial positioning of spherical fruits and improve positioning accuracy. Experimental results show that the MSA-Net model achieves a precision of 97.56, a recall of 92.21, and an [email protected] of 94.81 on a comprehensive dataset containing blueberries, lychees, strawberries, and tomatoes, demonstrating the ability to accurately recognize multiple types of fruits. Moreover, the introduction of the Hough Transform mechanism reduces the average localization error by 8.8 pixels and 3.5 pixels for fruit images at different distances, effectively improving the accuracy of fruit localization. Full article
(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture—2nd Edition)
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16 pages, 6003 KiB  
Article
GSE-YOLO: A Lightweight and High-Precision Model for Identifying the Ripeness of Pitaya (Dragon Fruit) Based on the YOLOv8n Improvement
by Zhi Qiu, Zhiyuan Huang, Deyun Mo, Xuejun Tian and Xinyuan Tian
Horticulturae 2024, 10(8), 852; https://doi.org/10.3390/horticulturae10080852 - 12 Aug 2024
Viewed by 1021
Abstract
Pitaya fruit is a significant agricultural commodity in southern China. The traditional method of determining the ripeness of pitaya by humans is inefficient, it is therefore of the utmost importance to utilize precision agriculture and smart farming technologies in order to accurately identify [...] Read more.
Pitaya fruit is a significant agricultural commodity in southern China. The traditional method of determining the ripeness of pitaya by humans is inefficient, it is therefore of the utmost importance to utilize precision agriculture and smart farming technologies in order to accurately identify the ripeness of pitaya fruit. In order to achieve rapid recognition of pitaya targets in natural environments, we focus on pitaya maturity as the research object. During the growth process, pitaya undergoes changes in its shape and color, with each stage exhibiting significant characteristics. Therefore, we divided the pitaya into four stages according to different maturity levels, namely Bud, Immature, Semi-mature and Mature, and we have designed a lightweight detection and classification network for recognizing the maturity of pitaya fruit based on the YOLOv8n algorithm, namely GSE-YOLO (GhostConv SPPELAN-EMA-YOLO). The specific methods include replacing the convolutional layer of the backbone network in the YOLOv8n model, incorporating attention mechanisms, modifying the loss function, and implementing data augmentation. Our improved YOLOv8n model achieved a detection and recognition accuracy of 85.2%, a recall rate of 87.3%, an F1 score of 86.23, and an mAP50 of 90.9%, addressing the issue of false or missed detection of pitaya ripeness in intricate environments. The experimental results demonstrate that our enhanced YOLOv8n model has attained a commendable level of accuracy in discerning pitaya ripeness, which has a positive impact on the advancement of precision agriculture and smart farming technologies. Full article
(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture—2nd Edition)
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