Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.3
3.6
Journals
Agriculture
Special Issues
Advances in Robotic Systems for Precision Orchard Operations
share announcement

Advances in Robotic Systems for Precision Orchard Operations

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

Deadline for manuscript submissions: 10 August 2026 | Viewed by 3566

Special Issue Editor

Prof. Dr. Hongjun Wang

E-Mail Website
Guest Editor
College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: robotic vision; deep-learning; autonomous navigation; field robot; robot planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The application of robotics in agriculture, particularly in orchards, represents a frontier in addressing labor shortages, increasing productivity, and promoting sustainable practices. Driven by advancements in artificial intelligence, sensing technologies, and intelligent machinery, precision orchard robotics has evolved from simple automation to complex systems capable of making autonomous decisions.

This Special Issue aims to compile cutting-edge research on the development and integration of robotic systems for precision orchard management. We seek to explore innovative solutions that enhance the efficiency, accuracy, and sustainability of fruit production. The scope of this Special Issue encompasses a wide range of technologies, including precision spraying, intelligent weeding, automated pruning, flower and fruit thinning, growth monitoring, pest and disease identification, yield estimation, field survey, remote sensing, and selective harvesting.

We encourage submissions that present original research on cutting-edge technologies, such as novel sensor fusion, advanced machine learning algorithms for perception and decision-making, robust robotic manipulation under uncertainty, and seamless multi-robot coordination. Studies on field validation, scalability, and the economic and environmental impacts of these systems are also highly relevant.

We invite contributions of original research articles, comprehensive reviews, and case studies that demonstrate significant scientific and practical advancements in orchard robotics. Interdisciplinary studies that bridge robotics, computer science, horticulture, and agricultural engineering are particularly welcome.

Prof. Dr. Hongjun Wang
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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

  • precision spraying
  • intelligent weeding
  • automated pruning
  • flower and fruit management
  • growth monitoring
  • pest and disease identification
  • yield estimation
  • field survey
  • remote sensing
  • selective harvesting

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

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

20 pages, 2855 KB  
Article
Investigating the Impact of Picking Modes on the Picking Process of Peach (Prunus persica) Using Experimental and Simulation Analysis
by Yufei Lin, Jie Wang, Li Tian, Hao Liang, Xiaping Fu and Chuanyu Wu
Agriculture 2026, 16(9), 979; https://doi.org/10.3390/agriculture16090979 - 29 Apr 2026
Viewed by 439
Abstract
To explore robotic peach picking in different modes, this study examined the effects of various peach picking modes on harvesting force and time. A finite element model of peach harvesting structure was established, and harvesting experiment parameters were based on the Box–Behnken design. [...] Read more.
To explore robotic peach picking in different modes, this study examined the effects of various peach picking modes on harvesting force and time. A finite element model of peach harvesting structure was established, and harvesting experiment parameters were based on the Box–Behnken design. Harvesting was simulated to collect response time and force data. Subsequently, the optimal harvesting rate under different picking modes was determined. Different picking modes were tested by simulating identical fruit harvesting in the laboratory at the optimal harvesting speed to determine the peak harvesting force and duration. The Bend mode had the lowest picking pressure and the shortest average picking time at 0.7 MPa and 4.2 s, respectively. The Pull and Twist modes had similar pressures and picking times at 1.2 and 1.1 MPa and 5.2 and 5.6 s, respectively. Harvesting in the orchard allowed for harvesting force and duration measurement under different picking modes. The differences in picking pressure and time among the three picking modes increased compared with those of simulated picking, with specific patterns being observed. Picking pressure appeared at P1max, and differences in picking time were prevalent during separation. This study offers valuable insights for future improvements in harvesting modes and efficiency enhancement. Full article
(This article belongs to the Special Issue Advances in Robotic Systems for Precision Orchard Operations)
►▼ Show Figures

Figure 1

18 pages, 6451 KB  
Article
YOLOv11n-GrapeLite: A Lightweight Multi-Variety Grape Recognition Model
by Yahui Luo, Guangsheng Gao, Wenwu Hu, Pin Jiang, Tie Zhang, Delin Shang, Xiangjun Zou, Guoshun Yang and Yuxuan Tan
Agriculture 2026, 16(7), 794; https://doi.org/10.3390/agriculture16070794 - 3 Apr 2026
Viewed by 534
Abstract
To address the challenges of rapid and accurate grape variety identification in natural orchard environments, along with the demand for efficient deployment on mobile devices, we propose in this paper YOLOv11n-GrapeLite, a lightweight model built upon an enhanced YOLOv11n architecture. First, an Efficient [...] Read more.
To address the challenges of rapid and accurate grape variety identification in natural orchard environments, along with the demand for efficient deployment on mobile devices, we propose in this paper YOLOv11n-GrapeLite, a lightweight model built upon an enhanced YOLOv11n architecture. First, an Efficient Channel Attention (ECA) mechanism is incorporated into the Neck layer. This mechanism adaptively recalibrates feature channel weights to emphasize those relevant to grape variety recognition, suppress background interference, and enhance target feature perception in complex scenes. Second, an adaptive downsampling (ADown) strategy is employed to replace the traditional convolutional downsampling module, reducing computational complexity while preserving critical features. Finally, the original C3k2 module is redesigned as a multi-scale convolution block (MSCB). This block integrates depthwise separable convolutions with multi-scale convolutions, which achieves significant parameter compression and enhances multi-scale feature extraction. Experimental results demonstrate that the proposed model achieves a mean average precision (mAP) of 91.5%, representing a 0.2% improvement over the original YOLOv11n, along with a 0.6% increase in recall. These results indicate outstanding robustness in complex field scenarios. The model’s parameter count was reduced to 1.87 million, computational complexity to 5.0 GFLOPS, and model size to 4.1 MB. These figures represent reductions of 27.8%, 23.1%, and 25.5%, respectively, compared to the original YOLOv11n, demonstrating significant lightweight optimization. Compared to mainstream models such as YOLOv6, YOLOv8n, YOLOv9s, YOLOV12, YOLOv13 and YOLOv26, the proposed model achieves superior performance in parameter count, computational load, and model size, while maintaining competitive detection accuracy. The YOLOv11n-GrapeLite model efficiently adapts to mobile terminal deployment, providing a feasible and efficient technical solution for real-time, precise identification of grape varieties in complex field scenarios. Full article
(This article belongs to the Special Issue Advances in Robotic Systems for Precision Orchard Operations)
►▼ Show Figures

Figure 1

31 pages, 7864 KB  
Article
Development of a General-Purpose AI-Powered Robotic Platform for Strawberry Harvesting
by Muhammad Tufail, Jamshed Iqbal and Rafiq Ahmad
Agriculture 2026, 16(7), 769; https://doi.org/10.3390/agriculture16070769 - 31 Mar 2026
Viewed by 803
Abstract
The integration of emerging technologies such as robotics and artificial intelligence (AI) has the potential to transform agricultural harvesting by improving efficiency, reducing waste, lowering labor dependency, and enhancing produce quality. This paper presents the development of an intelligent robotic berry harvesting system [...] Read more.
The integration of emerging technologies such as robotics and artificial intelligence (AI) has the potential to transform agricultural harvesting by improving efficiency, reducing waste, lowering labor dependency, and enhancing produce quality. This paper presents the development of an intelligent robotic berry harvesting system that combines deep learning–based perception with autonomous robotic manipulation for real-time strawberry harvesting. A computer vision pipeline based on the YOLOv11 segmentation model was developed and integrated into a Smart Mobile Manipulator (SMM) equipped with autonomous navigation, a 6-degree-of-freedom (6-DoF) xArm 6 robotic arm, and ROS middleware to enable real-time operation. Using a publicly available strawberry dataset comprising 2,800 images collected under ridge-planted cultivation conditions, the proposed YOLOv11-small segmentation model achieved 84.41% mAP@0.5, outperforming YOLOv11 object detection, Faster R-CNN, and RT-DETR in segmentation quality while maintaining real-time performance at 10 FPS on an NVIDIA Jetson Orin Nano edge GPU. A PCA-based fruit orientation and geometric analysis method achieved 86.5% localization accuracy on 200 test images. Controlled indoor harvesting experiments using synthetic strawberries demonstrated an overall harvesting success rate of 72% across 50 trials. The proposed system provides a general-purpose platform for berry harvesting in controlled environments, offering a scalable and efficient solution for autonomous harvesting. Full article
(This article belongs to the Special Issue Advances in Robotic Systems for Precision Orchard Operations)
►▼ Show Figures

Figure 1

25 pages, 15267 KB  
Article
3D Semantic Map Reconstruction for Orchard Environments Using Multi-Sensor Fusion
by Quanchao Wang, Yiheng Chen, Jiaxiang Li, Yongxing Chen and Hongjun Wang
Agriculture 2026, 16(4), 455; https://doi.org/10.3390/agriculture16040455 - 15 Feb 2026
Cited by 2 | Viewed by 1155
Abstract
Semantic point cloud maps play a pivotal role in smart agriculture. They provide not only core three-dimensional data for orchard management but also empower robots with environmental perception, enabling safer and more efficient navigation and planning. However, traditional point cloud maps primarily model [...] Read more.
Semantic point cloud maps play a pivotal role in smart agriculture. They provide not only core three-dimensional data for orchard management but also empower robots with environmental perception, enabling safer and more efficient navigation and planning. However, traditional point cloud maps primarily model surrounding obstacles from a geometric perspective, failing to capture distinctions and characteristics between individual obstacles. In contrast, semantic maps encompass semantic information and even topological relationships among objects in the environment. Furthermore, existing semantic map construction methods are predominantly vision-based, making them ill-suited to handle rapid lighting changes in agricultural settings that can cause positioning failures. Therefore, this paper proposes a positioning and semantic map reconstruction method tailored for orchards. It integrates visual, LiDAR, and inertial sensors to obtain high-precision pose and point cloud maps. By combining open-vocabulary detection and semantic segmentation models, it projects two-dimensional detected semantic information onto the three-dimensional point cloud, ultimately generating a point cloud map enriched with semantic information. The resulting 2D occupancy grid map is utilized for robotic motion planning. Experimental results demonstrate that on a custom dataset, the proposed method achieves 74.33% mIoU for semantic segmentation accuracy, 12.4% relative error for fruit recall rate, and 0.038803 m mean translation error for localization. The deployed semantic segmentation network Fast-SAM achieves a processing speed of 13.36 ms per frame. These results demonstrate that the proposed method combines high accuracy with real-time performance in semantic map reconstruction. This exploratory work provides theoretical and technical references for future research on more precise localization and more complete semantic mapping, offering broad application prospects and providing key technological support for intelligent agriculture. Full article
(This article belongs to the Special Issue Advances in Robotic Systems for Precision Orchard Operations)
►▼ 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