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Agronomy
Special Issues
Research Progress in Agricultural Robots in Arable Farming
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Research Progress in Agricultural Robots in Arable Farming

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 1 March 2026 | Viewed by 5507

Special Issue Editors

Dr. Xianping Guan

E-Mail Website
Guest Editor
Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang 212013, China
Interests: agricultural machinery design, detection, and control technology
Dr. Wan-Soo Kim

E-Mail Website
Guest Editor
Department of Smart Bio-Industrial Mechanical Engineering, Kyungpook National University (KNU), Daegu, 80 Daehak-ro, Buk-gu, Daegu, Republic of Korea
Interests: agricultural machinery; agricultural tractor; digital agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The continuous growth of the global population, the intensification of climate change challenges, and the increasing shortage of agricultural labor are profoundly reshaping the global food production system. In this context, agricultural robot technology, as the core pillar of smart agriculture, has become a revolutionary force in promoting the efficient and sustainable transformation of Arable Farming with its enormous potential for automation, precision, and intelligence. From precise sowing and variable fertilization in vast fields, to intelligent weed recognition and targeted removal in complex environments, real-time monitoring and yield prediction of crop health based on multi-source data, and intelligent harvesting, robot systems are deeply integrated into the entire chain of field production.

In this Special Issue, we welcome submissions focusing on innovative technologies and applications on Research Progress in Agricultural Robots in Arable Farming, including but not limited to the following: advanced perception technology in complex field scenarios, intelligent extraction and analysis of crop phenotype information, weed and pest detection and identification positioning, intelligent planning and decision-making models and methods based on artificial intelligence, advanced robots and cluster robot systems, and integrated testing of agricultural intelligent systems, to improve the productivity, efficiency, and sustainability of field agriculture.

We invite experts and researchers to submit original research, comments, and opinions on the theme of this Special Issue.

Dr. Xianping Guan
Dr. Wan-Soo Kim
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. Agronomy 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 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 robotics
  • arable farming
  • intelligent agriculture
  • agricultural perception

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Published Papers (3 papers)

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Research

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20 pages, 4430 KB  
Article
Path Tracking Controller and System Design for Agricultural Tractors Based on Improved Stanley and Sliding Mode Algorithms Considering Sideslip Compensation
by Anzhe Wang, Xin Ji, Qi Song, Xinhua Wei, Wenming Chen and Kun Wang
Agronomy 2025, 15(10), 2329; https://doi.org/10.3390/agronomy15102329 - 1 Oct 2025
Viewed by 436
Abstract
Global agriculture is confronting unprecedented pressures from population growth, diminishing arable land, and severe rural labor scarcity, necessitating the advancement of intelligent agricultural equipment. As a core component of precision farming, unmanned agricultural tractors demand highly accurate and robust path tracking control. However, [...] Read more.
Global agriculture is confronting unprecedented pressures from population growth, diminishing arable land, and severe rural labor scarcity, necessitating the advancement of intelligent agricultural equipment. As a core component of precision farming, unmanned agricultural tractors demand highly accurate and robust path tracking control. However, conventional methods often fail to cope with unstructured terrain and dynamic wheel slip under real field conditions. This paper proposes an extended state observer (ESO)-based improved Stanley guidance law, which incorporates real-time sideslip angle observation, adaptive preview-based path curvature compensation, and a sliding mode heading controller. The ESO estimates lateral slip caused by varying soil conditions, while the modified Stanley law utilizes look-ahead path information to proactively adjust the desired heading angle during high-curvature turns. Both co-simulation in Matlab-Carsim and field experiments demonstrate that the proposed method significantly reduces lateral tracking error and overshoot, outperforming classical algorithms such as fuzzy Stanley and sliding mode controller, especially in U-turn scenarios and under low-adhesion conditions. Full article
(This article belongs to the Special Issue Research Progress in Agricultural Robots in Arable Farming)
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Review

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30 pages, 2503 KB  
Review
A Systematic Review of 59 Field Robots for Agricultural Tasks: Applications, Trends, and Future Directions
by Mattia Fontani, Sofia Matilde Luglio, Lorenzo Gagliardi, Andrea Peruzzi, Christian Frasconi, Michele Raffaelli and Marco Fontanelli
Agronomy 2025, 15(9), 2185; https://doi.org/10.3390/agronomy15092185 - 13 Sep 2025
Viewed by 2374
Abstract
Climate change and labour shortage are re-shaping farming methods. Agricultural tasks are often hard, tedious and repetitive for operators, and farms struggle to find specialized operators for such works. For this and other reasons (i.e., the increasing costs of agricultural labour) more and [...] Read more.
Climate change and labour shortage are re-shaping farming methods. Agricultural tasks are often hard, tedious and repetitive for operators, and farms struggle to find specialized operators for such works. For this and other reasons (i.e., the increasing costs of agricultural labour) more and more farmers have decided to switch to autonomous (or semi-autonomous) field robots. In the past decade, an increasing number of robots has filled the market of agricultural machines all over the world. These machines can easily cover long and repetitive tasks, while operators can be employed in other jobs inside the farms. This paper reviews the current state-of-the-art of autonomous robots for agricultural operations, dividing them into categories based on main tasks, to analyze their main characteristics and their fields of applications. Seven main tasks were identified: multi-purpose, harvesting, mechanical weeding, pest control and chemical weeding, scouting and monitoring, transplanting and tilling-sowing. Field robots were divided into these categories, and different characteristics were analyzed, such as engine type, traction system, application field, safety sensors, navigation system, country of provenience and presence on the market. The aim of this review is to provide a global view on agricultural platforms developed in the past decade, analyzing their characteristics and providing future perspectives for next robotic platforms. The analysis conducted on 59 field robots, those already available on the market and not, revealed that one fifth of the platforms comes from Asia, and 63% of all of them are powered by electricity (rechargeable batteries, not solar powered) and that numerous platforms base their navigation system on RTK-GPS signal, 28 out of 59, and safety on LiDAR sensor (12 out of 59). This review considered machines of different size, highlighting different possible choices for field operations and tasks. It is difficult to predict market trends as several possibilities exist, like fleets of small robots or bigger size platforms. Future research and policies should focus on improving navigation and safety systems, reducing emissions and improving level of autonomy of robotic platforms. Full article
(This article belongs to the Special Issue Research Progress in Agricultural Robots in Arable Farming)
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54 pages, 2856 KB  
Review
Applications, Trends, and Challenges of Precision Weed Control Technologies Based on Deep Learning and Machine Vision
by Xiangxin Gao, Jianmin Gao and Waqar Ahmed Qureshi
Agronomy 2025, 15(8), 1954; https://doi.org/10.3390/agronomy15081954 - 13 Aug 2025
Viewed by 2205
Abstract
Advanced computer vision (CV) and deep learning (DL) are essential for sustainable agriculture via automated vegetation management. This paper methodically reviews advancements in these technologies for agricultural settings, analyzing their fundamental principles, designs, system integration, and practical applications. The amalgamation of transformer topologies [...] Read more.
Advanced computer vision (CV) and deep learning (DL) are essential for sustainable agriculture via automated vegetation management. This paper methodically reviews advancements in these technologies for agricultural settings, analyzing their fundamental principles, designs, system integration, and practical applications. The amalgamation of transformer topologies with convolutional neural networks (CNNs) in models such as YOLO (You Only Look Once) and Mask R-CNN (Region-Based Convolutional Neural Network) markedly enhances target recognition and semantic segmentation. The integration of LiDAR (Light Detection and Ranging) with multispectral imagery significantly improves recognition accuracy in intricate situations. Moreover, the integration of deep learning models with control systems, which include laser modules, robotic arms, and precision spray nozzles, facilitates the development of intelligent robotic mowing systems that significantly diminish chemical herbicide consumption and enhance operational efficiency relative to conventional approaches. Significant obstacles persist, including restricted environmental adaptability, real-time processing limitations, and inadequate model generalization. Future directions entail the integration of varied data sources, the development of streamlined models, and the enhancement of intelligent decision-making systems, establishing a framework for the advancement of sustainable agricultural technology. Full article
(This article belongs to the Special Issue Research Progress in Agricultural Robots in Arable Farming)
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