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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: 10 August 2025 | Viewed by 5286

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

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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 (4 papers)

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Research

21 pages, 6316 KiB  
Article
A Method for Tomato Ripeness Recognition and Detection Based on an Improved YOLOv8 Model
by Zhanshuo Yang, Yaxian Li, Qiyu Han, Haoming Wang, Chunjiang Li and Zhandong Wu
Horticulturae 2025, 11(1), 15; https://doi.org/10.3390/horticulturae11010015 - 27 Dec 2024
Viewed by 773
Abstract
With the rapid development of agriculture, tomatoes, as an important economic crop, require accurate ripeness recognition technology to enable selective harvesting. Therefore, intelligent tomato ripeness recognition plays a crucial role in agricultural production. However, factors such as lighting conditions and occlusion lead to [...] Read more.
With the rapid development of agriculture, tomatoes, as an important economic crop, require accurate ripeness recognition technology to enable selective harvesting. Therefore, intelligent tomato ripeness recognition plays a crucial role in agricultural production. However, factors such as lighting conditions and occlusion lead to issues such as low detection accuracy, false detections, and missed detections. Thus, a deep learning algorithm for tomato ripeness detection based on an improved YOLOv8n is proposed in this study. First, the improved YOLOv8 model is used for tomato target detection and ripeness classification. The RCA-CBAM (Region and Color Attention Convolutional Block Attention Module) module is introduced into the YOLOv8 backbone network to enhance the model’s focus on key features. By incorporating attention mechanisms across three dimensions—color, channel, and spatial attention—the model’s ability to recognize changes in tomato color and spatial positioning is improved. Additionally, the BiFPN (Bidirectional Feature Pyramid Network) module is introduced to replace the traditional PANet connection, which achieves efficient feature fusion across different scales of tomato skin color, size, and surrounding environment and optimizes the expression ability of the feature map. Finally, an Inner-FocalerIoU loss function is designed and integrated to address the difficulty of ripeness classification caused by class imbalance in the samples. The results show that the improved YOLOv8+ model is capable of accurately recognizing the ripeness level of tomatoes, achieving relatively high values of 95.8% precision value and 91.7% accuracy on the test dataset. It is concluded that the new model has strong detection performance and real-time detection. Full article
(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture—2nd Edition)
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21 pages, 29481 KiB  
Article
Fresh Tea Leaf-Grading Detection: An Improved YOLOv8 Neural Network Model Utilizing Deep Learning
by Zejun Wang, Yuxin Xia, Houqiao Wang, Xiaohui Liu, Raoqiong Che, Xiaoxue Guo, Hongxu Li, Shihao Zhang and Baijuan Wang
Horticulturae 2024, 10(12), 1347; https://doi.org/10.3390/horticulturae10121347 - 15 Dec 2024
Viewed by 1158
Abstract
To facilitate the realization of automated tea picking and enhance the speed and accuracy of tea leaf grading detection, this study proposes an improved YOLOv8 network for fresh tea leaf grading recognition. This approach integrates a Hierarchical Vision Transformer using Shifted Windows to [...] Read more.
To facilitate the realization of automated tea picking and enhance the speed and accuracy of tea leaf grading detection, this study proposes an improved YOLOv8 network for fresh tea leaf grading recognition. This approach integrates a Hierarchical Vision Transformer using Shifted Windows to replace segments of the original YOLOv8’s network architecture, thereby alleviating the computational load of dense image processing tasks and reducing computational expenses. The incorporation of an Efficient Multi-Scale Attention Module with Cross-Spatial Learning serves to attenuate the influence of irrelevant features in complex backgrounds, which in turn, elevates the model’s detection Precision. Additionally, the substitution of the loss function with SIoU facilitates a more rapid model convergence and a more precise pinpointing of defect locations. The empirical findings indicate that the enhanced YOLOv8 algorithm has achieved a marked improvement in metrics such as Precision, Recall, F1, and mAP, with increases of 3.39%, 0.86%, 2.20%, and 2.81% respectively, when juxtaposed with the original YOLOv8 model. Moreover, in external validations, the FPS enhancements over the original YOLOv8, YOLOv5, YOLOX, Faster RCNN, and SSD deep-learning models are 6.75 Hz, 10.84 Hz, 12.79 Hz, 28.24 Hz, and 21.57 Hz, respectively, and the mAP improvements in practical detection are 2.79%, 2.92%, 3.08%, 7.07%, and 3.84% respectively. The refined model not only ensures efficient and accurate tea-grading recognition but also boasts high recognition rates and swift detection capabilities, thereby establishing a foundation for the development of tea-picking robots and tea quality grading devices. Full article
(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture—2nd Edition)
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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 1124
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 mAP@0.5 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
Cited by 2 | Viewed by 1458
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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