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

Deadline for manuscript submissions: 31 October 2023 | Viewed by 3625

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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

Dr. Lufeng Luo

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Guest Editor

School of Mechatronics Engineering and Automation, Foshan University, Foshan 528000, China
Interests: robot target recognition; visual cognitive computing; flexible actuator design
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Dear Colleagues,

As an important part of modern agriculture, horticulture also plays an important 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 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 improve the accuracy and efficiency of management, thus increasing the output.

This Special Issue focuses on the current intelligent technology and equipment 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 put forward 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 1800 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

Published Papers (5 papers)

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Research

Open AccessArticle

A Comparative Analysis of the Grafting Efficiency of Watermelon with a Grafting Machine

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Horticulturae 2023, 9(5), 600; https://doi.org/10.3390/horticulturae9050600 - 19 May 2023

Viewed by 387

Abstract

The rising age of the population in rural China and the labor intensity of grafting have resulted in a decrease in the number of grafters and a subsequent increase in their wages. Manual grafting can no longer satisfy the increasing demand for watermelon-grafted transplanting; thus, machine grafting will be an effective alternative. In order to accelerate the implementation of machine grafting in China, a comparative analysis between the automatic grafting machine (model 2TJGQ-800) and traditional hand grafting was conducted. The reliability and feasibility of machine grafting were evaluated through a comprehensive evaluation of the production capacity and grafting seedling quality. This study focuses on the grafting application of watermelon plug-tray seedlings. The scion and rootstock seeds were sown on 9 November 2022. Grafting experiments using an automatic grafting machine, skilled workers, and ordinary workers were conducted with the root-pruned one-cotyledon grafting method on 24 November 2022. The results showed that the machine grafting had a high uniformity and grafting speed. The grafting speed of the grafting machine was 774 plant·h⁻¹ and 1.65–2.55-fold higher than the hand grafting. With training, workers can improve their grafting speed, but it will still be slower than machine grafting. In addition, there was no significant difference in the grafting survival rate between the machine grafting and hand grafting. However, using machine grafting, the success rate decreased from 100% to 90.07% and the rootstock regrowth rate increased from 18.44% to 72.69%. Incomplete rootstock cutting, clip supply failure, and grafting drop failure are the three main factors that result in machine grafting failure. In conclusion, the grafting machine has advantages in terms of grafting speed and uniformity. Upon improving the accuracy of the cutting mechanism and grafting success rate, it will be adopted by commercial nurseries. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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Open AccessArticle

GA-YOLO: A Lightweight YOLO Model for Dense and Occluded Grape Target Detection

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Horticulturae 2023, 9(4), 443; https://doi.org/10.3390/horticulturae9040443 - 28 Mar 2023

Viewed by 514

Abstract

Picking robots have become an important development direction of smart agriculture, and the position detection of fruit is the key to realizing robot picking. However, the existing detection models have the shortcomings of missing detection and slow detection speed when detecting dense and occluded grape targets. Meanwhile, the parameters of the existing model are too large, which makes it difficult to deploy to the mobile terminal. In this paper, a lightweight GA-YOLO model is proposed. Firstly, a new backbone network SE-CSPGhostnet is designed, which greatly reduces the parameters of the model. Secondly, an adaptively spatial feature fusion mechanism is used to address the issues of difficult detection of dense and occluded grapes. Finally, a new loss function is constructed to improve detection efficiency. In 2022, a detection experiment was carried out on the image data collected in the Bagui rural area of Guangxi Zhuang Autonomous Region, the results demonstrate that the GA-YOLO model has an mAP of 96.87%, detection speed of 55.867 FPS and parameters of 11.003 M. In comparison to the model before improvement, the GA-YOLO model has improved mAP by 3.69% and detection speed by 20.245 FPS. Additionally, the GA-YOLO model has reduced parameters by 82.79%. GA-YOLO model not only improves the detection accuracy of dense and occluded targets but also lessens model parameters and accelerates detection speed. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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Open AccessArticle

Reasearch on Kiwi Fruit Flower Recognition for Efficient Pollination Based on an Improved YOLOv5 Algorithm

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Horticulturae 2023, 9(3), 400; https://doi.org/10.3390/horticulturae9030400 - 20 Mar 2023

Viewed by 805

Abstract

A close relationship has been observed between the growth and development of kiwi fruit and the pollination of the kiwi flower. Flower overlap, flower tilt, and other problems will affect this plant’s pollination success rate. A pollination model based on YOLOv5 was developed to improve the pollination of kiwi flowers. The K-means++ clustering method was used to cluster the anchors closer to the target size, which improved the speed of the algorithm. A convolutional block module attention mechanism was incorporated to improve the extraction accuracy with respect to kiwi flower features and effectively reduce the missed detection and error rates. The optimization of the detection function improves the recognition of flower overlap and the accuracy of flower tilt angle calculation and accurately determines flower coordinates, pollination point coordinates, and pollination angles. The experimental results show that the predicted value of the YOLOv5s model is 96.7% and that its recognition accuracy is the highest. Its mean average precision value is up to 89.1%, its F1 score ratio is 90.12%, and its memory requirements are the smallest (only 20 MB). The YOLOv5s model achieved the highest recognition accuracy as determined through a comparison experiment of the four sets of analysed models, thereby demonstrating its ability to facilitate the efficient target pollination of kiwi flowers. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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Open AccessArticle

HeLoDL: Hedgerow Localization Based on Deep Learning

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Horticulturae 2023, 9(2), 227; https://doi.org/10.3390/horticulturae9020227 - 08 Feb 2023

Viewed by 567

Abstract

Accurate localization of hedges in 3D space is a key step in automatic pruning. However, due to the irregularity of the hedge shape, the localization accuracy based on traditional algorithms is poor. In this paper, we propose a deep learning approach based on [...] Read more.

H e L o D L

. Specifically, we first project the hedge point cloud top-down as a single image and, then, augment the image with morphological operations and rotation. Finally, we trained a convolutional neural network,

H e L o D L

, based on transfer learning, to regress the center axis and radius of the hedge. In addition, we propose an evaluation metric OIoU that can respond to the radius error, as well as the circle center error in an integrated way. In our test set,

H e L o D L

achieved an accuracy of 90.44% within the error tolerance, which greatly exceeds the 61.74% of the state-of-the-art algorithm. The average OIoU of

H e L o D L

is 92.65%; however, the average OIoU of the best conventional algorithm is 83.69%. Extensive experiments demonstrated that

H e L o D L

shows considerable accuracy in the 3D spatial localization of irregular models. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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Open AccessArticle

Research on Real-Time Automatic Picking of Ground-Penetrating Radar Image Features by Using Machine Learning

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Horticulturae 2022, 8(12), 1116; https://doi.org/10.3390/horticulturae8121116 - 28 Nov 2022

Viewed by 754

Abstract

Hard foreign objects such as bricks, wood, metal materials, and plastics in orchard soil can affect the operational safety of garden machinery. Ground-Penetrating Radar (GPR) is widely used for the detection of hard foreign objects in soil due to its advantages of non-destructive detection (NDT), easy portability, and high efficiency. At present, the degree of automatic identification applied in soil-oriented foreign object detection based on GPR falls short of the industry’s expectations. To further enhance the accuracy and efficiency of soil-oriented foreign object detection, we combined GPR and intelligent technology to conduct research on three aspects: acquiring real-time GPR images, using the YOLOv5 algorithm for real-time target detection and the coordinate positioning of GPR images, and the construction of a detection system based on ground-penetrating radar and the YOLOv5 algorithm that automatically detects target characteristic curves in ground-penetrating radar images. In addition, taking five groups of test results of detecting different diameters of rebar inside the soil as an example, the obtained average error of detecting the depth of rebar using the detection system is within 0.02 m, and the error of detecting rebar along the measuring line direction from the location of the starting point of GPR detection is within 0.08 m. The experimental results show that the detection system is important for identifying and positioning foreign objects inside the soil. Full article

(This article belongs to the Special Issue Application of Smart Technology and Equipment in Horticulture)

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