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Agriculture
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Advances in Precision Agriculture in Orchard
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Advances in Precision Agriculture in Orchard

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 11290

Special Issue Editors

Dr. Xiongzhe Han

E-Mail Website
Guest Editor
Department of Biosystems Engineering, College of Agriculture and Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
Interests: agricultural robotics; UAV-based remote sensing; autonomous navigation in agriculture; AI-driven plant disease detection; site-specific variable-rate application; precision agriculture; deep learning for crop monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianqiang Lu

E-Mail Website
Guest Editor
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Interests: artificial intelligence in smart agriculture; edge intelligent hardware; intelligent control of unmanned vehicles; multi machine collaboration; optimized route planning; remote sensing imagery of UAV

Special Issue Information

Dear Colleagues,

Advances in precision agriculture are transforming orchard production through integrated, data-driven approaches that enhance efficiency, resilience, and sustainability across all stages of management. Moving beyond traditional practices, modern precision orchard agriculture combines intelligent sensing, automation, analytics, and connected systems to enable informed, site-specific decisions. By leveraging multi-source data, environmental monitoring, and digital modeling, growers can comprehensively oversee orchard development, health, and resource use in real time.

This Special Issue, “Advances in Precision Agriculture in Orchard”, aims to highlight research and innovations supporting intelligent and sustainable orchard systems. The scope covers growth monitoring, pest and disease management, adaptive harvesting, intelligent logistics, and postharvest handling. We especially welcome cutting-edge studies integrating multi-modal sensing, machine learning, digital twin modeling, autonomous navigation, and predictive analytics. Submissions may include original research, reviews, methodological advances, and case studies demonstrating interdisciplinary solutions and new directions for precision orchard agriculture.

Dr. Xiongzhe Han
Prof. Dr. Jianqiang Lu
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 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 agriculture
  • smart orchards
  • intelligent sensing
  • digital innovation
  • automation
  • data management
  • sustainable practices
  • decision support
  • adaptive harvesting
  • climate resilience

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

Published Papers (5 papers)

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Research

33 pages, 75745 KB  
Article
DTNet: A Novel Infrared-Visible Fruit Object Detection Method Based on Dual-Modal Feature Interaction Fusion and Transformer Decoding
by Ziqian Di and Guoxiang Sun
Agriculture 2026, 16(10), 1044; https://doi.org/10.3390/agriculture16101044 - 11 May 2026
Viewed by 396
Abstract
Fruit detection in orchard environments is often challenged by illumination variation, leaf occlusion, cluttered backgrounds, and dense distributions of small targets, which limit the robustness of visible-light detectors in practical applications. To address these issues, this study proposes DTNet, an end-to-end RGB-IR dual-modal [...] Read more.
Fruit detection in orchard environments is often challenged by illumination variation, leaf occlusion, cluttered backgrounds, and dense distributions of small targets, which limit the robustness of visible-light detectors in practical applications. To address these issues, this study proposes DTNet, an end-to-end RGB-IR dual-modal detection framework for close-range fruit instance recognition. Rather than relying on single-modal appearance cues, DTNet improves detection robustness by jointly exploiting cross-modal complementary perception, fine-grained local detail enhancement, and global relational modeling, thereby strengthening the representation of small, partially occluded, and illumination-sensitive fruit targets. In addition, a multimodal balanced detection loss is introduced to improve optimization stability under class imbalance and hard-sample interference. Experiments on a grape RGB-IR object detection dataset show that DTNet achieves a Precision of 0.9132, a Recall of 0.8931, an mAP@0.5 of 0.9552, and an mAP@0.5:0.95 of 0.8001, outperforming competing methods overall. On an additional tomato RGB-IR dataset, DTNet also maintains stable detection accuracy, indicating favorable adaptability to another fruit RGB-IR detection task. The results indicate that DTNet is a promising approach for robust RGB-IR fruit detection in complex orchard environments. Full article
(This article belongs to the Special Issue Advances in Precision Agriculture in Orchard)
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27 pages, 4859 KB  
Article
Trajectory Tracking Control of an Agricultural Tracked Vehicle Based on Nonlinear Model Predictive Control
by Huijun Zeng, Shilei Lyu, Peng Gao, Shangshang Cheng, Songmao Gao, Jiahong Chen, Zijie Li, Ziheng Wei and Zhen Li
Agriculture 2026, 16(7), 816; https://doi.org/10.3390/agriculture16070816 - 7 Apr 2026
Viewed by 427
Abstract
Accurate trajectory tracking is challenging for tracked agricultural vehicles in orchards. Uneven terrain, track slip, and vehicle posture variations are the main causes, often leading to model mismatch and degraded control performance. To address these issues, this paper proposes an improved nonlinear model [...] Read more.
Accurate trajectory tracking is challenging for tracked agricultural vehicles in orchards. Uneven terrain, track slip, and vehicle posture variations are the main causes, often leading to model mismatch and degraded control performance. To address these issues, this paper proposes an improved nonlinear model predictive control (NMPC) strategy integrated with curvature feedforward compensation for trajectory tracking of tracked agricultural vehicles under uneven terrain conditions. An enhanced kinematic model based on the instantaneous center of rotation is developed by incorporating vehicle roll and pitch angles, and track slip parameters are estimated online using a Levenberg–Marquardt optimization method to improve prediction accuracy. Furthermore, curvature feedforward information derived from the reference trajectory is embedded into the NMPC objective function to provide anticipatory control inputs and reduce computational burden. Simulation results demonstrate that compared to conventional NMPC, the proposed method reduces the mean and standard deviation of tracking error by 30.28% and 32.46% respectively, while decreasing the mean and standard deviation of heading error by 37.27% and 35.05%. Concurrently, the maximum of optimize solution time is significantly reduced, effectively resolving tracking accuracy degradation caused by system solution timeouts. Field experiments conducted under different load conditions further validate that the proposed control strategy significantly reduces lateral, longitudinal, and heading tracking errors compared with conventional NMPC, confirming its effectiveness and robustness for tracked agricultural vehicle trajectory tracking in complex orchard environments. Full article
(This article belongs to the Special Issue Advances in Precision Agriculture in Orchard)
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27 pages, 6205 KB  
Article
FAL-YOLO: A Keypoint Detection Method for Harvest Crates in Farmland Environments Based on an Improved YOLOv8-Pose Algorithm
by Jing Huang, Shengjun Shi, Shilei Lyu, Zhihui Chen, Yikai Lin and Zhen Li
Agriculture 2026, 16(5), 570; https://doi.org/10.3390/agriculture16050570 - 2 Mar 2026
Viewed by 593
Abstract
To address the challenges of harvest crate localization caused by varying illumination, partial occlusion, and background interference in unstructured farmland environments, as well as the high costs and low efficiency associated with traditional manual harvesting, this paper proposes FAL-YOLO, a lightweight keypoint detection [...] Read more.
To address the challenges of harvest crate localization caused by varying illumination, partial occlusion, and background interference in unstructured farmland environments, as well as the high costs and low efficiency associated with traditional manual harvesting, this paper proposes FAL-YOLO, a lightweight keypoint detection model. Using YOLOv8n-Pose as the baseline framework, the model integrates a C2f-ContextGuided backbone and a Slim-Neck feature fusion layer. Furthermore, a LSCD-LQE lightweight detection head is designed, and an Inner-MPDIoU loss function is introduced to enhance keypoint detection performance under complex backgrounds and occluded conditions. Experimental results on the self-constructed farmland harvest crate dataset indicate that FAL-YOLO requires only 1.71 M parameters and 4.5 GFLOPs of computational cost, representing reductions of 44.5% and 45.8% compared to YOLOv8n-Pose, while achieving an mAP@0.5 of 94.9%, corresponding to an improvement of 1.2%. Additionally, by establishing correspondences between keypoints and the 3D model through the PnP algorithm, the 3D pose of the crate can be reconstructed, providing reliable spatial input for robotic arm manipulation. The results demonstrate that FAL-YOLO achieves an effective balance between model lightweightness and detection accuracy, providing an efficient solution for automatic identification and grasping of harvest crates in farmland environments. Full article
(This article belongs to the Special Issue Advances in Precision Agriculture in Orchard)
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17 pages, 11104 KB  
Article
Lightweight Improvements to the Pomelo Image Segmentation Method for Yolov8n-seg
by Zhen Li, Baiwei Cao, Zhengwei Yu, Qingting Jin, Shilei Lyu, Xiaoyi Chen and Danting Mao
Agriculture 2026, 16(2), 186; https://doi.org/10.3390/agriculture16020186 - 12 Jan 2026
Viewed by 796
Abstract
Instance segmentation in agricultural robotics requires a balance between real-time performance and accuracy. This study proposes a lightweight pomelo image segmentation method based on the YOLOv8n-seg model integrated with the RepGhost module. A pomelo dataset consisting of 5076 samples was constructed through systematic [...] Read more.
Instance segmentation in agricultural robotics requires a balance between real-time performance and accuracy. This study proposes a lightweight pomelo image segmentation method based on the YOLOv8n-seg model integrated with the RepGhost module. A pomelo dataset consisting of 5076 samples was constructed through systematic image acquisition, annotation, and data augmentation. The RepGhost architecture was incorporated into the C2f module of the YOLOv8-seg backbone network to enhance feature reuse capabilities while reducing computational complexity. Experimental results demonstrate that the YOLOv8-seg-RepGhost model enhances efficiency without compromising accuracy: parameter count is reduced by 16.5% (from 3.41 M to 2.84 M), computational load decreases by 14.8% (from 12.8 GFLOPs to 10.9 GFLOPs), and inference time is shortened by 6.3% (to 15 ms). The model maintains excellent detection performance with bounding box mAP50 at 97.75% and mask mAP50 at 97.51%. The research achieves both high segmentation efficiency and detection accuracy, offering core support for developing visual systems in harvesting robots and providing an effective solution for deep learning-based fruit target recognition and automated harvesting applications. Full article
(This article belongs to the Special Issue Advances in Precision Agriculture in Orchard)
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25 pages, 7018 KB  
Article
LiDAR-IMU Sensor Fusion-Based SLAM for Enhanced Autonomous Navigation in Orchards
by Seulgi Choi, Xiongzhe Han, Eunha Chang and Haetnim Jeong
Agriculture 2025, 15(17), 1899; https://doi.org/10.3390/agriculture15171899 - 7 Sep 2025
Cited by 1 | Viewed by 7376
Abstract
Labor shortages and uneven terrain in orchards present significant challenges to autonomous navigation. This study proposes a navigation system that integrates Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) data to enhance localization accuracy and map stability through Simultaneous Localization and [...] Read more.
Labor shortages and uneven terrain in orchards present significant challenges to autonomous navigation. This study proposes a navigation system that integrates Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) data to enhance localization accuracy and map stability through Simultaneous Localization and Mapping (SLAM). To minimize distortions in LiDAR scans caused by ground irregularities, real-time tilt correction was implemented based on IMU feedback. Furthermore, the path planning module was improved by modifying the Rapidly-Exploring Random Tree (RRT) algorithm. The enhanced RRT generated smoother and more efficient trajectories with quantifiable improvements: the average shortest path length was 2.26 m, compared to 2.59 m with conventional RRT and 2.71 m with A* algorithm. Tracking performance also improved, achieving a root mean square error of 0.890 m and a maximum lateral deviation of 0.423 m. In addition, yaw stability was strengthened, as heading fluctuations decreased by approximately 7% relative to the standard RRT. Field results validated the robustness and adaptability of the proposed system under real-world agricultural conditions. These findings highlight the potential of LiDAR–IMU sensor fusion and optimized path planning to enable scalable and reliable autonomous navigation for precision agriculture. Full article
(This article belongs to the Special Issue Advances in Precision Agriculture in Orchard)
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