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: 5 February 2026 | Viewed by 1747

Special Issue Editors


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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
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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

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Keywords

  • precision agriculture
  • smart orchards
  • intelligent sensing
  • digital innovation
  • automation
  • data management
  • sustainable practices
  • decision support
  • adaptive harvesting
  • climate resilience

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Published Papers (1 paper)

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Research

26 pages, 6286 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
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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