Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.6
3.6
Journals
Agriculture
Special Issues
Agricultural Machinery and Technology for Fruit Tree Management
share announcement

Agricultural Machinery and Technology for Fruit Tree Management

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: 25 June 2024 | Viewed by 4890

Special Issue Editors

Prof. Dr. Zhen Li

E-Mail Website
Guest Editor
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Interests: intelligent orchard machinery; mountainous orchard management; plant protection machinery; Internet of Things; fruit tree modern cultivation techniques
Prof. Dr. Tao Wen

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha 410004, China
Interests: E-nose; spectral detection; sensors and intelligent detection technology; postharvest technology and equipment; orchard machinery
Dr. Shilei Lyu

E-Mail Website
Guest Editor
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Interests: orchard transport; smart orchard; intelligent computing; agricultural Internet of Things; computer vision

Special Issue Information

Dear Colleagues,

Fruits represent a vital dietary component due to their abundant nutrient elements, thereby serving as a crucial factor for human well-being and demonstrating notable economic significance. The global production of fruits has been steadily escalating, while simultaneously confronting mounting challenges such as labor scarcity and escalating production costs. Consequently, the mechanization of fruit tree management assumes an increasingly pivotal role. Given the geographical diversity of fruit species and the intricate and distinctive nature of fruit orchard management, numerous scholars worldwide have undertaken extensive and profound investigations. This collective scholarly effort has culminated in the development of a diverse array of machinery and equipment for fruit production, furnishing a wealth of effective solutions to mechanizing fruit tree management.

This Special Issue encompasses a topic of utmost relevance, namely, the trend towards standardized smart orchard construction. Its primary focus lies in elucidating the critical facets of fruit tree management, including transportation, weeding, pruning, flower thinning, plant protection, and harvesting. The Issue serves as a platform for sharing the latest advancements in intelligent machinery and technological research, thereby facilitating the exploration of efficacious strategies to enhance the overall management proficiency of fruit trees and orchards. For this reason, it welcomes highly interdisciplinary quality studies from disparate research fields including overall machine design, key components and parts, mechanism and simulation analysis, intelligent technology, and other related fields. We welcome the submission of original research articles and reviews.

Prof. Dr. Zhen Li
Prof. Dr. Tao Wen
Dr. Shilei Lyu
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. Agriculture 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 2600 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

  • agricultural machinery
  • information technology
  • standardized orchard
  • intelligent orchard
  • transportation machinery
  • thinning machinery
  • weeding machinery
  • plant protection machinery
  • harvesting machinery
  • orchard irrigation
  • insect information
  • growth information

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 9695 KiB  
Article
A Lightweight Algorithm for Recognizing Pear Leaf Diseases in Natural Scenes Based on an Improved YOLOv5 Deep Learning Model
by Jianian Li, Zhengquan Liu and Dejin Wang
Agriculture 2024, 14(2), 273; https://doi.org/10.3390/agriculture14020273 - 07 Feb 2024
Viewed by 757
Abstract
The precise detection of diseases is crucial for the effective treatment of pear trees and to improve their fruit yield and quality. Currently, recognizing plant diseases in complex backgrounds remains a significant challenge. Therefore, a lightweight CCG-YOLOv5n model was designed to efficiently recognize [...] Read more.
The precise detection of diseases is crucial for the effective treatment of pear trees and to improve their fruit yield and quality. Currently, recognizing plant diseases in complex backgrounds remains a significant challenge. Therefore, a lightweight CCG-YOLOv5n model was designed to efficiently recognize pear leaf diseases in complex backgrounds. The CCG-YOLOv5n model integrates a CA attention mechanism, CARAFE up-sampling operator, and GSConv into YOLOv5n. It was trained and validated using a self-constructed dataset of pear leaf diseases. The model size and FLOPs are only 3.49 M and 3.8 G, respectively. The [email protected] is 92.4%, and the FPS is up to 129. Compared to other lightweight indicates that the models, the experimental results demonstrate that the CCG-YOLOv5n achieves higher average detection accuracy and faster detection speed with a smaller computation and model size. In addition, the robustness comparison test CCG-YOLOv5n model has strong robustness under various lighting and weather conditions, including frontlight, backlight, sidelight, tree shade, and rain. This study proposed a CCG-YOLOv5n model for accurately detecting pear leaf diseases in complex backgrounds. The model is suitable for use on mobile terminals or devices. Full article
(This article belongs to the Special Issue Agricultural Machinery and Technology for Fruit Tree Management)
►▼ Show Figures

Figure 1

21 pages, 4219 KiB  
Article
Optimization and Experimental Study of Structural Parameters for a Low-Damage Packing Device on an Apple Harvesting Platform
by Zixu Chen, Hongjian Zhang, Huawei Yang, Yinfa Yan, Jingwei Sun, Guangze Zhao, Jinxing Wang and Guoqiang Fan
Agriculture 2023, 13(9), 1653; https://doi.org/10.3390/agriculture13091653 - 22 Aug 2023
Viewed by 1008
Abstract
To address the issues of low efficiency and high damage rates during apple harvesting and packing, a parameter optimization experiment was conducted on a low-damage packing device for an apple harvesting platform based on Adams 2019 software. The aim was to reduce the [...] Read more.
To address the issues of low efficiency and high damage rates during apple harvesting and packing, a parameter optimization experiment was conducted on a low-damage packing device for an apple harvesting platform based on Adams 2019 software. The aim was to reduce the mechanical damage to apples during the packing process. Firstly, kinematics and energetics analyses of the apple packing process were performed, and a mathematical model for damage energy was established to identify the main factors and their ranges that influence the mechanical damage to apples. Secondly, using the fruit damage rate and packing efficiency as the evaluation criteria, a second-order orthogonal rotating regression experiment was conducted with the inclination angle of the fruit conveying tube, the inner wall radius of the fruit conveying tube, and the length of the fruit conveying tube as the experimental factors. Regression mathematical models were established to assess the relationship between the evaluation criteria and the experimental factors. Finally, the impact of each experimental factor on the evaluation criteria was analyzed to determine the optimal structural parameters for the low-damage packing device of the apple harvesting platform, and validation experiments were conducted. The results showed that when the inclination angle of the fruit conveying tube was 47°, the inner wall radius of the fruit conveying tube was 84 mm and the length of the fruit conveying tube was 0.12 m, the average fruit damage rate was minimized at 7.2%, and the average packing efficiency was maximized at 1925 kg/h. These results meet the requirements for apple harvesting operations, and the research findings can serve as a reference for the structural design and packing operation parameter optimization of apple harvesting platforms. Full article
(This article belongs to the Special Issue Agricultural Machinery and Technology for Fruit Tree Management)
►▼ Show Figures

Figure 1

18 pages, 4954 KiB  
Article
Detection and Classification of Citrus Fruit Infestation by Bactrocera dorsalis (Hendel) Using a Multi-Path Vis/NIR Spectroscopy System
by Dapeng Li, Jiang Long, Ziye Tang, Longbo Han, Zhongliang Gong, Liang Wen, Hailong Peng and Tao Wen
Agriculture 2023, 13(8), 1642; https://doi.org/10.3390/agriculture13081642 - 21 Aug 2023
Viewed by 1154
Abstract
In this study, a multi-path Vis/NIR spectroscopy system was developed to detect the presence of Bactrocera dorsalis (Hendel) infestations of citrus fruit. Spectra were acquired for 252 citrus fruit, 126 of which were infested. Two hundred and fifty-two spectra were acquired for modeling [...] Read more.
In this study, a multi-path Vis/NIR spectroscopy system was developed to detect the presence of Bactrocera dorsalis (Hendel) infestations of citrus fruit. Spectra were acquired for 252 citrus fruit, 126 of which were infested. Two hundred and fifty-two spectra were acquired for modeling in their un-infested stage, slightly infested stage, and seriously infested stage. The location of the infestation is unclear, and considering the impact of the light path on the location of the infestation, each citrus fruit was tested in three orientations (i.e., fruit stalks facing upward (A), fruit stalks facing horizontally (B), and fruit stalks facing downward (C)). Classification models based on joint X-Y distance, multiple transmittance calibration, competitive adaptive reweighted sampling, and partial least squares discriminant analysis (SPXY-MSC-CARS-PLS-DA) were developed on the spectra of each light path, and the average spectra of the four light paths was calculated, to compare their performance in infestation classification. The results show the classification result changed with the light path and fruit orientation. The average spectra for each fruit orientation consistently gave better classification results, with overall accuracies of 92.9%, 89.3%, and 90.5% for orientations A, B, and C, respectively. Moreover, the best model had a Kappa value of 0.89, and gave 95.2%, 80.1%, and 100.0% accuracy for un-infested, slightly infested, and seriously infested citrus fruit. Furthermore, the classification results for infested citrus fruits were better when using the average spectra than using the spectrum of each single light path. Therefore, the multi-path Vis/NIR spectroscopy system is conducive to the detection of B. dorsalis infestation in citrus fruits. Full article
(This article belongs to the Special Issue Agricultural Machinery and Technology for Fruit Tree Management)
►▼ Show Figures

Figure 1

13 pages, 2263 KiB  
Article
Parameters Optimization and Performance Evaluation Model of Air-Assisted Electrostatic Sprayer for Citrus Orchards
by Xiuyun Xue, Kaixiang Zeng, Nengchao Li, Qin Luo, Yihang Ji, Zhen Li, Shilei Lyu and Shuran Song
Agriculture 2023, 13(8), 1498; https://doi.org/10.3390/agriculture13081498 - 27 Jul 2023
Viewed by 1095
Abstract
Citrus orchards in Southeast Asia are commonly grown in hilly areas, where the terrain is unsuitable for the operation of crop protection machinery. Conventional spraying equipment used in hilly orchards have a poor deposition effect. In this paper, a new air-assisted electrostatic sprayer [...] Read more.
Citrus orchards in Southeast Asia are commonly grown in hilly areas, where the terrain is unsuitable for the operation of crop protection machinery. Conventional spraying equipment used in hilly orchards have a poor deposition effect. In this paper, a new air-assisted electrostatic sprayer was designed for hilly citrus orchards. The orthogonal method was conducted to determine the optimal spray parameters of the sprayer. To evaluate the spray performance of the optimized air-assisted electrostatic sprayer, field tests were carried out on a citrus orchard with various cultivation patterns. Based on the data of the field tests, a comprehensive evaluation model was constructed to quantitatively analyze the performance of the sprayer. Results indicate that the optimal parameters are a spray pressure of 0.5 MPa, applied voltage of 9 kV and air flow velocity of 10 m/s. The optimized air-assisted electrostatic sprayer has the best performance in the citrus under dense fence cultivation pattern, followed by dense dwarf cultivation pattern. Comparing to the other sprayers tested, the air-assisted electrostatic sprayer greatly improves the spray coverage on the leaf surfaces (abaxial and adaxial) under various cultivation patterns. Full article
(This article belongs to the Special Issue Agricultural Machinery and Technology for Fruit Tree Management)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop