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Agriculture
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Agricultural Machinery and Technology for Fruit Orchard Management
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Agricultural Machinery and Technology for Fruit Orchard Management

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1714

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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianian Li

E-Mail Website
Guest Editor
Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China
Interests: intelligent detection and control in agriculture; agricultural artificial intelligence; intelligent equipment and technology for integrated water and fertilizer management; agricultural sensors and agricultural internet of things
Prof. 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 Issues, Collections and Topics in MDPI journals

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 their economic significance. The global production of fruits has been steadily increasing, while simultaneously confronting mounting challenges such as labor scarcity and escalating production costs. Consequently, the mechanization of fruit tree and orchard management holds an increasingly pivotal role. Given the geographical diversity of fruit species and the intricate and distinctive nature of fruit orchard management, numerous scholars have carried out extensive and thorough investigations around the world. 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 is a continuation of the previous Special Issue “Agricultural Machinery and Technology for Fruit Orchard Management” and addresses a topic of the utmost relevance, namely, the trend towards standardized smart orchard construction. Its primary focus lies in elucidating critical facets of fruit orchard management, including transportation, weeding, pruning, flower thinning, plant protection, and harvesting. The Special 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, we welcome interdisciplinary and high-quality studies from disparate research fields, including overall machine design, key components and parts, mechanism and simulation analyses, and intelligent technology. Original research articles and reviews are accepted.

Prof. Dr. Zhen Li
Prof. Dr. Jianian Li
Prof. 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 semimonthly 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 orchards
  • intelligent orchards
  • transportation machinery
  • thinning machinery
  • weeding machinery
  • plant protection machinery
  • harvesting machinery
  • orchard irrigation
  • insect information
  • growth information

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

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Research

23 pages, 14677 KiB  
Article
Design of and Experimentation on an Intelligent Intra-Row Obstacle Avoidance and Weeding Machine for Orchards
by Weidong Jia, Kaile Tai, Xiang Dong, Mingxiong Ou and Xiaowen Wang
Agriculture 2025, 15(9), 947; https://doi.org/10.3390/agriculture15090947 - 27 Apr 2025
Viewed by 169
Abstract
Based on the current issues of difficulty in clearing intra-row weeds in orchards, inaccurate sensor detection, and the inability to adjust the row spacing depth, this study designs an intelligent intra-row obstacle avoidance and weeding machine for orchards. We designed the weeding machine’s [...] Read more.
Based on the current issues of difficulty in clearing intra-row weeds in orchards, inaccurate sensor detection, and the inability to adjust the row spacing depth, this study designs an intelligent intra-row obstacle avoidance and weeding machine for orchards. We designed the weeding machine’s sensor device, depth-limiting device, row spacing adjustment mechanism, joystick-based obstacle avoidance mechanism, weeding shovel, and hydraulic system. The sensor device integrates non-contact sensors and a mechanical tactile structure, which overcomes the instability of non-contact detection and avoids the risk of collision obstacle avoidance by the weeding parts. The weeding shovel can be adapted to the environments of orchards with small plant spacing. The combination of the sensor device and the obstacle avoidance mechanism realizes flexible obstacle avoidance. We used Ansys Workbench to conduct static and vibration modal analyses on the chassis of the in-field weeding machine. On this basis, through topology optimization, the chassis quality of the weeding machine is reduced by 8%, which realizes the goal of light weight and ensures the stable operation of the machinery. To further optimize the weeding operation parameters, we employed the Box–Behnken design response surface analysis, with weeding coverage as the optimization target. We systematically explored the effects of forward speed, hydraulic cylinder extension speed, and retraction speed on the weeding efficiency. The optimal operational parameter combination determined by this study for the weeding machine is as follows: forward speed of 0.5 m/s, hydraulic cylinder extension speed of 11.5 cm/s, and hydraulic cylinder retraction speed of 8 cm/s. Based on the theoretical analysis and scenario simulations, we validated the performance of the weeding machine through field experiments. The results show that the weeding machine, while exhibiting excellent obstacle avoidance performance, can achieve a maximum weeding coverage of 84.6%. This study provides a theoretical foundation and technical support for the design and development of in-field mechanical weeding, which is of great significance for achieving intelligent orchard management and further improving fruit yield and quality. Full article
(This article belongs to the Special Issue Agricultural Machinery and Technology for Fruit Orchard Management)
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19 pages, 3399 KiB  
Article
Comparative Analysis of CNN-Based Semantic Segmentation for Apple Tree Canopy Size Recognition in Automated Variable-Rate Spraying
by Tantan Jin, Su Min Kang, Na Rin Kim, Hye Ryeong Kim and Xiongzhe Han
Agriculture 2025, 15(7), 789; https://doi.org/10.3390/agriculture15070789 - 6 Apr 2025
Viewed by 491
Abstract
Efficient pest control in orchards is crucial for preserving crop quality and maximizing yield. A key factor in optimizing automated variable-rate spraying is accurate tree canopy size estimation, which helps reduce pesticide overuse while minimizing environmental and health risks. This study evaluates the [...] Read more.
Efficient pest control in orchards is crucial for preserving crop quality and maximizing yield. A key factor in optimizing automated variable-rate spraying is accurate tree canopy size estimation, which helps reduce pesticide overuse while minimizing environmental and health risks. This study evaluates the performance of two advanced convolutional neural networks, PP-LiteSeg and fully convolutional networks (FCNs), for segmenting tree canopies of varying sizes—small, medium, and large—using short-term dense-connection networks (STDC1 and STDC2) as backbones. A dataset of 305 field-collected images was used for model training and evaluation. The results show that FCNs with STDC backbones outperform PP-LiteSeg, delivering superior semantic segmentation accuracy and background classification. The STDC1-based model excels in precision variable-rate spraying, achieving an Intersection-over-Union of up to 0.75, Recall of 0.85, and Precision of approximately 0.85. Meanwhile, the STDC2-based model demonstrates greater optimization stability and faster convergence, making it more suitable for resource-constrained environments. Notably, the STDC2-based model significantly enhances canopy-background differentiation, achieving a background classification Recall of 0.9942. In contrast, PP-LiteSeg struggles with small canopy detection, leading to reduced segmentation accuracy. These findings highlight the potential of FCNs with STDC backbones for automated apple tree canopy recognition, advancing precision agriculture and promoting sustainable pesticide application through improved variable-rate spraying strategies. Full article
(This article belongs to the Special Issue Agricultural Machinery and Technology for Fruit Orchard Management)
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21 pages, 6180 KiB  
Article
RT-DETR-MCDAF: Multimodal Fusion of Visible Light and Near-Infrared Images for Citrus Surface Defect Detection in the Compound Domain
by Jingxi Luo, Zhanwei Yang, Ying Cao, Tao Wen and Dapeng Li
Agriculture 2025, 15(6), 630; https://doi.org/10.3390/agriculture15060630 - 17 Mar 2025
Viewed by 507
Abstract
The accurate detection of citrus surface defects is essential for automated citrus sorting to enhance the commercialization of the citrus industry. However, previous studies have only focused on single-modal defect detection using visible light images (RGB) or near-infrared light images (NIR), without considering [...] Read more.
The accurate detection of citrus surface defects is essential for automated citrus sorting to enhance the commercialization of the citrus industry. However, previous studies have only focused on single-modal defect detection using visible light images (RGB) or near-infrared light images (NIR), without considering the feature fusion between these two modalities. This study proposed an RGB-NIR multimodal fusion method to extract and integrate key features from both modalities to enhance defect detection performance. First, an RGB-NIR multimodal dataset containing four types of citrus surface defects (cankers, pests, melanoses, and cracks) was constructed. Second, a Multimodal Compound Domain Attention Fusion (MCDAF) module was developed for multimodal channel fusion. Finally, MCDAF was integrated into the feature extraction network of Real-Time DEtection TRansformer (RT-DETR). The experimental results demonstrated that RT-DETR-MCDAF achieved Precision, Recall, mAP@0.5, and mAP@0.5:0.95 values of 0.914, 0.919, 0.90, and 0.937, respectively, with an average detection performance of 0.598. Compared with the model RT-DETR-RGB&NIR, which used simple channel concatenation fusion, RT-DETR-MCDAF improved the performance by 1.3%, 1.7%, 1%, 1.5%, and 1.7%, respectively. Overall, the proposed model outperformed traditional channel fusion methods and state-of-the-art single-modal models, providing innovative insights for commercial citrus sorting. Full article
(This article belongs to the Special Issue Agricultural Machinery and Technology for Fruit Orchard Management)
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