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Horticulturae
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Advances in Intelligent Orchard
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Advances in Intelligent Orchard

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Fruit Production Systems".

Deadline for manuscript submissions: closed (25 September 2024) | Viewed by 13563

Special Issue Editors

Prof. Dr. Jun Chen

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Interests: intelligent harvesting; intelligent agricultural equipment; automatic navigation of agricultural machinery
Prof. Dr. Shanjun Li

E-Mail Website
Guest Editor
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Interests: hilly orchard transportation machinery; fruit sorting; nondestructive testing; citrus postharvest quality management
Dr. Fuxi Shi

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Interests: orchard intelligent machinery; precision pollination technology; relaxation atomization theory
Dr. Shuo Zhang

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Interests: intelligent agricultural equipment; precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fruits are essential foods for humans, providing a lot of trace elements and vitamins. With the increasing consumption demand and the exaggeration of planting scale, labor-intensive fruit production faces great challenges. Orchard planting production is gradually developing towards mechanized and intelligent operation, especially in the process of orchard construction, mid-term management, and harvest. The integration of agricultural machinery and agronomy in fruit planting has also become a bright direction that must be considered in research on mechanization and intelligence.

This Special Issue of Horticulturae will provide a current overview of the most significant research carried out in the field of advances in intelligent orchards. Scientists from all over the world are invited to submit original research and review articles related to the following topics: intelligent fruit harvesting technology and equipment, precision pollination, integration of agricultural machinery and agronomy in fruit planting process production, and other related intelligent management technology in the process of orchard planting and production.

Prof. Dr. Jun Chen
Prof. Dr. Shanjun Li
Dr. Fuxi Shi
Dr. Shuo Zhang
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 fruit harvesting
  • precision pollination
  • intelligent management technology and equipment in fruit planting
  • integration of agricultural machinery and agronomy in orchards

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Published Papers (7 papers)

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Research

Jump to: Review

16 pages, 3639 KiB  
Article
Application of Ethephon Manually or via Drone Enforces Bud Dormancy and Enhances Flowering Response to Chilling in Litchi (Litchi chinensis Sonn.)
by Bingyi Wen, Cailian Deng, Qi Tian, Jianzhong Ouyang, Renfang Zeng, Huicong Wang and Xuming Huang
Horticulturae 2024, 10(10), 1109; https://doi.org/10.3390/horticulturae10101109 - 18 Oct 2024
Viewed by 552
Abstract
Ethephon (2-chloroethylphosphonic acid) is frequently used for flush management in order to maximize flowering in litchi. However, the optimal dosage of ethephon, which balances between flush control effect and the detrimental effect on leaves, is unknown. This study aimed to identify the optimal [...] Read more.
Ethephon (2-chloroethylphosphonic acid) is frequently used for flush management in order to maximize flowering in litchi. However, the optimal dosage of ethephon, which balances between flush control effect and the detrimental effect on leaves, is unknown. This study aimed to identify the optimal ethephon dosage and test more efficient ethephon application methods, using a drone for flush control and flowering promotion in litchi. The effects of a single manual full-tree spray of 250, 500 or 1000 mg/L of ethephon in early November on the bud break rate, leaf drop rate, net photosynthetic rate, LcFT1 expression and floral induction (panicle emergence rate and panicle number) in ‘Jingganghongnuo’ litchi were examined in the season of 2021–2022. In the season of 2022–2023, the effects of drone application of 1000 mg/L of ethephon in early November on bud growth and floral induction were observed. The results showed that the manual ethephon treatments were effective at enforcing bud dormancy and elongating the dormancy period and that the effects were positively dependent on dosage. One manual spray of 1000 mg/L of ethephon in late autumn enabled a dormancy period of 6 weeks. The treatments advanced seasonal abscission of old leaves in winter and caused short-term suppression on photosynthesis within 2 weeks after treatment. Ethephon treatments, especially at 1000 mg/L, enhanced the expression of LcFT1 in the mature leaves and promoted floral induction reflected by earlier panicle emergence and increased panicle emergence rate and number in the terminal shoot. The floral promotion effect was also positively dosage dependent. The cumulative chilling hours below 15 °C from the date of treatment to the occurrence of a 20% panicle emergence rate were lowered in ethephon treatments. A drone spray of 1000 mg/L of ethephon solution consumed a sixth of the manual spray solution volume and was two thirds less effective in suppressing bud break compared with manual spraying. However, it achieved a significant flowering promotion effect comparable to traditional manual spraying. The results suggest that ethephon application enhanced flowering responsiveness to chilling as well as enforced bud dormancy. The application of ethephon with a drone proved to be an efficient method for flush control and flower promotion. Full article
(This article belongs to the Special Issue Advances in Intelligent Orchard)
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25 pages, 12585 KiB  
Article
MTS-YOLO: A Multi-Task Lightweight and Efficient Model for Tomato Fruit Bunch Maturity and Stem Detection
by Maonian Wu, Hanran Lin, Xingren Shi, Shaojun Zhu and Bo Zheng
Horticulturae 2024, 10(9), 1006; https://doi.org/10.3390/horticulturae10091006 - 22 Sep 2024
Cited by 1 | Viewed by 1479
Abstract
The accurate identification of tomato maturity and picking positions is essential for efficient picking. Current deep-learning models face challenges such as large parameter sizes, single-task limitations, and insufficient precision. This study proposes MTS-YOLO, a lightweight and efficient model for detecting tomato fruit bunch [...] Read more.
The accurate identification of tomato maturity and picking positions is essential for efficient picking. Current deep-learning models face challenges such as large parameter sizes, single-task limitations, and insufficient precision. This study proposes MTS-YOLO, a lightweight and efficient model for detecting tomato fruit bunch maturity and stem picking positions. We reconstruct the YOLOv8 neck network and propose the high- and low-level interactive screening path aggregation network (HLIS-PAN), which achieves excellent multi-scale feature extraction through the alternating screening and fusion of high- and low-level information while reducing the number of parameters. Furthermore, We utilize DySample for efficient upsampling, bypassing complex kernel computations with point sampling. Moreover, context anchor attention (CAA) is introduced to enhance the model’s ability to recognize elongated targets such as tomato fruit bunches and stems. Experimental results indicate that MTS-YOLO achieves an F1-score of 88.7% and an [email protected] of 92.0%. Compared to mainstream models, MTS-YOLO not only enhances accuracy but also optimizes the model size, effectively reducing computational costs and inference time. The model precisely identifies the foreground targets that need to be harvested while ignoring background objects, contributing to improved picking efficiency. This study provides a lightweight and efficient technical solution for intelligent agricultural picking. Full article
(This article belongs to the Special Issue Advances in Intelligent Orchard)
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19 pages, 21316 KiB  
Article
Study on Target Detection Method of Walnuts during Oil Conversion Period
by Xiahui Fu, Juxia Wang, Fengzi Zhang, Weizheng Pan, Yu Zhang and Fu Zhao
Horticulturae 2024, 10(3), 275; https://doi.org/10.3390/horticulturae10030275 - 12 Mar 2024
Viewed by 1069
Abstract
The colors of walnut fruits and leaves are similar in the oil transformation period, and the fruits are easily blocked by the branches and leaves. On the basis of the improved YOLOv7-tiny, a detection model is proposed and integrated into an Android application [...] Read more.
The colors of walnut fruits and leaves are similar in the oil transformation period, and the fruits are easily blocked by the branches and leaves. On the basis of the improved YOLOv7-tiny, a detection model is proposed and integrated into an Android application to solve the problem of walnut identification. Ablation experiments conducted with three improved strategies show that the strategies can effectively enhance the performance of the model. In terms of combinatorial optimization, the YOLOv7-tiny detection model that combines FasterNet and LightMLP modules works excellently. Its AP50 and AP50–95 are 3.1 and 4 percentage points (97.4% and 77.3%, respectively) higher than those of the original model. YOLOv7-tiny’s model size and number of parameters are reduced by 14.6% and 14.4%, respectively, relative to those of the original model, and its detection time decreases to 15.4 ms. The model has good robustness and generalization ability and can provide a technical reference for intelligent real-time detection of walnuts during the oil conversion period. Full article
(This article belongs to the Special Issue Advances in Intelligent Orchard)
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14 pages, 10048 KiB  
Article
Theoretical Analysis and Experimental Research on the Apple Auto-Orientation Based on Flexible Roller
by Tongyun Luo, Jianguo Zhou, Shuo Zhang, Jun Chen, Guangrui Hu and Adilet Sugirbay
Horticulturae 2023, 9(11), 1235; https://doi.org/10.3390/horticulturae9111235 - 16 Nov 2023
Viewed by 1630
Abstract
After automatic in-field picking, apple stem shortening requires fixing the apple position and maintaining a relatively stable posture, which puts high demands on the automatic apple-orienting structure. In this paper, a novel dual roller compact apple field orientation structure with dual rollers rotating [...] Read more.
After automatic in-field picking, apple stem shortening requires fixing the apple position and maintaining a relatively stable posture, which puts high demands on the automatic apple-orienting structure. In this paper, a novel dual roller compact apple field orientation structure with dual rollers rotating in the same direction is proposed. It can realize the uniform orientation of apples after automatic picking in any attitude, and the apple auto-orientation phenomenon is theoretically analyzed based on the accurately established apple model, then the apple orientation test platform was set up and a monocular camera combined with YOLOv5m was used to determine the time of apple orientation. The results showed that 70.21% and 96.81% of the apples were respectively oriented within 7 s and 28 s with only two flexible rollers rotating in the corresponding direction. All the apples were oriented, and 95.24% of them moved along the axis toward the calyx end. The generalizability of the apple orientation device for different shapes of apples was then verified, and the relationship between the shape characteristics of apples and orientation speed was later illustrated. A structural basis was finally presented for automatic stem shortening and surface damage detection in the apple field. Full article
(This article belongs to the Special Issue Advances in Intelligent Orchard)
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15 pages, 3485 KiB  
Article
Classification and Identification of Apple Leaf Diseases and Insect Pests Based on Improved ResNet-50 Model
by Xiaohua Zhang, Haolin Li, Sihai Sun, Wenfeng Zhang, Fuxi Shi, Ruihua Zhang and Qin Liu
Horticulturae 2023, 9(9), 1046; https://doi.org/10.3390/horticulturae9091046 - 16 Sep 2023
Cited by 5 | Viewed by 2680
Abstract
Automaticidentification and prevention of leaf diseases and insect pests on fruit crops represent a key trend in the development of smart agriculture. In order to address the limitations of existing models with low identification rates of apple leaf diseases and insect pests, a [...] Read more.
Automaticidentification and prevention of leaf diseases and insect pests on fruit crops represent a key trend in the development of smart agriculture. In order to address the limitations of existing models with low identification rates of apple leaf diseases and insect pests, a novel identification model based on an improved ResNet-50 architecture was proposed, which incorporated the coordinate attention (CA) module and weight-adaptive multi-scale feature fusion (WAMFF) to enhance the ResNet-50’s image feature extraction capabilities. Transfer learning and online data enhancement are employed to boost the model’s generalization ability. The proposed model achieved a top-1 accuracy rate of 98.32% on the basis of AppleLeaf9 datasets, which is 4.58% higher than the value from the original model, and the improved model can effectively improve the localization of lesion features. Furthermore, compared with mainstream deep networks, such as AlexNet, VGG16, DenseNet, MNASNet, and GoogLeNet on the same dataset, the top-1 accuracy rate increased by 7.3%, 3.19%, 4.98%, 6.04% and 3.87%, respectively. The experimental results demonstrate that the improved model is effective in improving the identification accuracy of apple leaf diseases and insect pests and enhancing the model’s effective feature extraction capabilities. Full article
(This article belongs to the Special Issue Advances in Intelligent Orchard)
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13 pages, 2505 KiB  
Article
A Distance Measurement Approach for Large Fruit Picking with Single Camera
by Jie Liu, Dianzhuo Zhou, Yifan Wang, Yan Li and Weiqi Li
Horticulturae 2023, 9(5), 537; https://doi.org/10.3390/horticulturae9050537 - 28 Apr 2023
Viewed by 1425
Abstract
Target ranging is the premise for manipulators to complete agronomic operations such as picking and field management; however, complex environmental backgrounds and changing crop shapes increase the difficulty of obtaining target distance information based on binocular vision or depth cameras. In this work, [...] Read more.
Target ranging is the premise for manipulators to complete agronomic operations such as picking and field management; however, complex environmental backgrounds and changing crop shapes increase the difficulty of obtaining target distance information based on binocular vision or depth cameras. In this work, a method for ranging large-sized fruit based on monocular vision was proposed to provide a low-cost and low-computation alternative solution for the fruit thinning or picking robot. The regression relationships between the changes in the number of pixels occupied by the target area and the changes in the imaging distance were calculated based on the images of square-shaped checkerboards and circular-shaped checkerboards with 100 cm2, 121 cm2, 144 cm2, 169 cm2, 196 cm2, 225 cm2, 256 cm2, 289 cm2, and 324 cm2 as the area, respectively. The 918 checkerboard images were collected by the camera within the range from 0.25 m to 1.5 m, with 0.025 m as the length of each moving step, and analyzed in MATLAB to establish the ranging models. A total of 2448 images of four oval watermelons, four pyriform pomelos, and four oblate pomelos, as the representatives of large fruit with different shapes, were used to evaluate and optimize the performance of the models. The images of the front were the input, while the imaging distances were the output. The results showed that the absolute error would be less than 0.06 m for both models and would linearly increase with a decrease in the distance. The relative error could be controlled at 5%. The results proved the proposed monocular method could be a solution for the ranging of large fruit targets. Full article
(This article belongs to the Special Issue Advances in Intelligent Orchard)
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Review

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25 pages, 1311 KiB  
Review
Management Information Systems for Tree Fruit—1: A Review
by Hari Krishna Dhonju, Kerry Brian Walsh and Thakur Bhattarai
Horticulturae 2024, 10(1), 108; https://doi.org/10.3390/horticulturae10010108 - 22 Jan 2024
Cited by 2 | Viewed by 3608
Abstract
A farm management information system (MIS) entails record keeping based on a database management system, typically using a client-server architecture, i.e., an information system, IS, coupled with a variety of tools/methods/models for the support of operational management. The current review adopts a multivocal [...] Read more.
A farm management information system (MIS) entails record keeping based on a database management system, typically using a client-server architecture, i.e., an information system, IS, coupled with a variety of tools/methods/models for the support of operational management. The current review adopts a multivocal approach to consider academic and commercial developments in MISs for orchard management, based primarily on the refereed literature but extending to grey literature and interviews of Australian mango orchard managers. Drivers for orchard MIS development include increasing the orchard size and management complexity, including regulatory requirements around labour, chemical spray use and fertilisation. The enablers include improvements in within-orchard communications, distributed (web) delivery systems using desktop and mobile devices, and sensor systems and predictive models, e.g., for pest management. Most orchard MIS-related publications target the commodities of apple, grape, mango and olive in the context of management of plant health (pest and disease), plant development, irrigation and labour management. Harvest forecast and MIS modules are only now beginning to emerge, in contrast to a long history of use in grain production. The commercial systems trend towards an incorporation of financial information, an integration of data from multiple sources and a provision of dashboards that are tailored to the user. Requirements for industry adoption of a MIS are discussed in terms of technical and design features, with a focus on usability and scalability. Full article
(This article belongs to the Special Issue Advances in Intelligent Orchard)
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