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AgriEngineering
Special Issues
Intelligent Perception, Decision-Making, and Precision Operation in Agriculture: Technologies and...
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Intelligent Perception, Decision-Making, and Precision Operation in Agriculture: Technologies and Applications

A special issue of AgriEngineering (ISSN 2624-7402).

Deadline for manuscript submissions: 31 January 2027 | Viewed by 2133

Editors

Dr. Youqiang Ding

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Guest Editor
Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Interests: agricultural automation; precision agriculture; smart seeding and fertilization
Dr. Yunxia Wang

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Guest Editor
Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Interests: agricultural automation; soil parameters detection; smart seeding and fertilization
Dr. Xiantao He

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Guest Editor
China Agricultural University, College of Engineering, Beijing 100083, China
Interests: intelligent maize seeding technology; soil–machine–plant information sensing
Dr. Chunji Xie

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Guest Editor
Nanjing Agricultural University, College of Engineering, Nanjing 210031, China
Interests: electric drive seeding technology; operation information perception technology; sensor technology
Dr. Long Li

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Guest Editor
Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China
Interests: sensor; non-destructive detection; agricultural automation

Special Issue Information

Dear Colleagues,

The integration of advanced technologies into the agricultural sector has witnessed remarkable progress in recent years, driving the transformation toward smart agriculture characterized by intelligent perception, data-driven decision-making, and precision operations. These developments are crucial for addressing global challenges such as food security, resource efficiency, and environmental sustainability. However, the effective implementation of these technologies still faces significant hurdles, including technical bottlenecks, system integration complexity, and scalability issues. This Special Issue, titled ​​“Intelligent Perception, Decision-Making and Precision Operation in Agriculture: Technologies and Applications”,​​ aims to gather cutting-edge research and innovative applications that advance the theory and practice of smart agriculture, providing solutions to enhance agricultural productivity, resilience, and sustainability.

The Special Issue will feature original research articles, comprehensive reviews, and case studies that demonstrate novel methodologies, experimental validations, and scalable solutions. Topics of interest include, but are not limited to, the following:

​Advanced Sensing and Perception:​​ Sensors for soil, crop phenotyping, animal health, and environmental monitoring.

​AI and Decision Models:​​ Algorithms for yield prediction, disease detection, and decision support for seeding, transplanting, fertilization, and spraying.

​Precision Operation Technologies:​​ Variable-rate application systems, intelligent machinery, and robotics for automated field operations.

​Smart Farming Platforms:​​ IoT systems, digital twins, and knowledge graphs for integrated farm management.

​Sustainable and Small-Scale Solutions:​​ Low-cost technologies for smallholders and solutions supporting green, low-carbon agriculture.

Dr. Youqiang Ding
Dr. Yunxia Wang
Dr. Xiantao He
Dr. Chunji Xie
Dr. Long Li
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AgriEngineering 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 1800 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

  • intelligent perception
  • decision-making
  • precision operation
  • environmental sensors
  • biological and phenotypic sensors
  • quality and machinery sensors
  • advanced algorithms
  • sophisticated modeling
  • integrated platforms and knowledge graphs
  • smart machinery and robotics
  • variable-rate technology
  • integrated system applications

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

Published Papers (2 papers)

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Research

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27 pages, 20728 KB  
Article
Enhanced A* Pathfinding Using Distance-Dependent Octile Annealing for Mobile Robot Navigation in Agricultural Field Terrains
by Antonios Chatzisavvas and Minas Dasygenis
AgriEngineering 2026, 8(6), 223; https://doi.org/10.3390/agriengineering8060223 - 2 Jun 2026
Viewed by 310
Abstract
The A* algorithm is widely adopted across agriculture, robotics, and GPS navigation for efficient route planning, yet it faces challenges in balancing search efficiency with path quality. To address these limitations, we introduce Octile–Annealed, a novel heuristic that augments the classic Octile distance [...] Read more.
The A* algorithm is widely adopted across agriculture, robotics, and GPS navigation for efficient route planning, yet it faces challenges in balancing search efficiency with path quality. To address these limitations, we introduce Octile–Annealed, a novel heuristic that augments the classic Octile distance with a distance-dependent annealing weight. Specifically, Octile–Annealed scales the Octile metric by a smooth function of the current node’s Euclidean distance to the final location, yielding a heuristic that is gentle near the target and more directive when far away. This design retains the geometric fidelity of Octile, accelerates search convergence in open regions, and preserves guidance in constrained corridors. Beyond discrete planning, we incorporate adaptive Bézier smoothing to post-process the grid path into a collision-free, curvature-friendly trajectory. This is particularly relevant in agricultural environments (e.g., orchard rows and cross-aisles), where machines must follow efficient routes without abrupt turns that could slow operations or risk crop damage. We benchmark Octile–Annealed against three established baselines—Euclidean and Octile—on orchard-like grids of varying sizes and obstacle patterns. The results show that Octile–Annealed consistently reduces computation time while maintaining competitive raw path lengths and producing short, smooth Bézier trajectories. Overall, the proposed heuristic enhances A*’s operational efficiency and route quality, making it well-suited for complex, structured agricultural layouts and for general navigation tasks that benefit from smooth post-processing. However, it must be acknowledged that these comparative performance metrics are strictly limited to simulated grid cases; consequently, comprehensive validation using actual field data remains necessary to fully confirm their practical applicability under real-world agricultural conditions. Full article
(This article belongs to the Special Issue Intelligent Perception, Decision-Making, and Precision Operation in Agriculture: Technologies and Applications)
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Review

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26 pages, 3861 KB  
Review
Mechanization and Intelligent Technologies for Ginger Harvesting: Evolution, Frontiers, and Prospects
by Haiyang Shen, Guangyu Xue, Gongpu Wang, Wenhao Zheng, Lianglong Hu, Yanhua Zhang and Baoliang Peng
AgriEngineering 2026, 8(3), 112; https://doi.org/10.3390/agriengineering8030112 - 15 Mar 2026
Viewed by 1391
Abstract
Driven by agricultural labor shortages and rising quality requirements, ginger harvesting increasingly demands high-throughput, low-damage operations and a reliable supply chain. This review summarizes harvesting modes and harvester types used in ginger production, with emphasis on critical process modules: digging and lifting, soil [...] Read more.
Driven by agricultural labor shortages and rising quality requirements, ginger harvesting increasingly demands high-throughput, low-damage operations and a reliable supply chain. This review summarizes harvesting modes and harvester types used in ginger production, with emphasis on critical process modules: digging and lifting, soil disintegration and cleaning, vine cutting and anti-tangling, gentle conveying, and collection. We compare major technical routes in terms of field capacity, control of soil and foreign materials, damage mitigation, and reliability under continuous operation, and identify the conditions under which each route performs best. Drawing on advances in harvesting systems for other root and bulb crops, we outline transferable approaches for intelligent sensing, precision control, and system-level integration. We then propose an online monitoring and closed-loop regulation framework for strongly coupled conditions, such as heavy clay soils, plastic-mulch residues, and vine interference. Key bottlenecks include limited cross-regional adaptability, persistent trade-offs between low damage and high throughput, cost constraints on intelligent functions, and the lack of shared datasets and standardized evaluation protocols. Future progress should be anchored in integrated equipment sets and supporting operating specifications, guided by multi-source sensing-based quality indicators and interpretable control strategy libraries, to reduce harvest losses, stabilize marketable quality, improve operational efficiency, and enable scalable adoption. Full article
(This article belongs to the Special Issue Intelligent Perception, Decision-Making, and Precision Operation in Agriculture: Technologies and Applications)
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