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Agriculture
Special Issues
Soil-Machine Systems and Its Related Digital Technologies Application
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Soil-Machine Systems and Its Related Digital Technologies Application

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 838

Special Issue Editors

Dr. Ju-Seok Nam

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Guest Editor
Department of Biosystems Engineering, Kangwon National University, 1 Kangwondaehak-gil, Gangwon-do, Chuncheon 24341, Republic of Korea
Interests: agricultural engineering; agricultural ergonomics; agricultural field machinery; digital agriculture; soil–machine systems; terramechanics
Special Issues, Collections and Topics in MDPI journals
Dr. Anjin Chang

E-Mail Website
Guest Editor
Department of Agricultural Sciences and Engineering, Tennessee State University, Nashville, TN 37209, USA
Interests: precision agriculture; remote sensing; geospatial engineering; UAS; data science/management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The mechanization of agricultural works has greatly contributed to the improvement of agricultural productivity and the reduction in production costs. Since the beginning of mechanization, various kinds of agricultural machinery related to soil preparation, sowing, harvesting, post-harvesting, etc., have been developed. In addition, customized agricultural machines that are suitable for cultivation type and soil characteristics of each country and region have been developed. Agricultural machinery, unlike other industrial machinery, targets living organisms and operates on the soil, so it should be designed in consideration of the interaction with the soil. It is possible to optimally design agricultural machinery by understanding both the characteristics of the soil and the characteristics of the mechanical system. Furthermore, in the fourth industrial revolution digital agriculture using various types of data obtainable from different sensors is also being applied to the agriculture domain. Cutting-edge agricultural machinery such as unoccupied aircraft systems (UAS, also known as drone), autonomous tractors, and automated harvesting robots are being developed and commercialized for cost-effective production and sustainable agriculture, based on an understanding of both digital agriculture technologies such as sensors, AI, and ICT technologies and soil-machine systems.

This Special Issue focuses on research regarding soil–machine systems and digital agriculture technologies, including development of new agricultural equipment, application of new technologies to existing systems, integration of big data and AI into machine systems, and development of new concept and technology in precision agriculture. The scope is from traditional to cutting-edge agricultural systems. Soil-related and data-driven research is also of interest. Both original research articles and comprehensive reviews are welcome.

Dr. Ju-Seok Nam
Dr. Anjin Chang
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. 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

  • digital agriculture
  • artificial intelligence
  • deep learning
  • machine vision
  • soil-machine systems
  • biosystems engineering
  • precision agriculture
  • machine vision
  • smart farming
  • soil and crops

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Published Papers (2 papers)

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Research

22 pages, 3432 KiB  
Article
Tracking Accuracy Evaluation of Autonomous Agricultural Tractors via Rear Three-Point Hitch Estimation Using a Hybrid Model of EKF Transformer
by Eun-Kuk Kim, Tae-Ho Han, Jun-Ho Lee, Cheol-Woo Han and Ryu-Gap Lim
Agriculture 2025, 15(14), 1475; https://doi.org/10.3390/agriculture15141475 - 9 Jul 2025
Viewed by 287
Abstract
The objective of this study was to improve measurement accuracy in the evaluation of autonomous agricultural tractor performance by addressing external disturbances, such as sensor installation errors, vibrations, and heading-induced bias that occur during the measurement of the conventional rear three-point hitch (Rear [...] Read more.
The objective of this study was to improve measurement accuracy in the evaluation of autonomous agricultural tractor performance by addressing external disturbances, such as sensor installation errors, vibrations, and heading-induced bias that occur during the measurement of the conventional rear three-point hitch (Rear 3-Point) system. To mitigate these disturbances, the measurement point was relocated to the cab, where external interference is comparatively minimal. However, in compliance with the ISO 12188 standard, the Rear 3-Point system must be used as the reference measurement point. Therefore, its coordinates were indirectly estimated using an extended Kalman filter (EKF) and artificial intelligence (AI)-based techniques. A hybrid model was developed in which a transformer-based AI model was trained using the Rear 3-Point coordinates predicted by EKF as the ground truth. While traditional time-series models, such as LSTM and GRU, show limitations in predicting nonlinear data, the application of an attention mechanism was found to enhance prediction performance by effectively learning temporal dependencies and vibration patterns. The experimental results show that the EKF-based estimation achieved a precision of RMSE 1.6 mm, a maximum error of 12.6 mm, and a maximum standard deviation of 3.9 mm compared to actual measurements. From the perspective of experimental design, the proposed hybrid model was able to predict the trajectory of the autonomous agricultural tractor with significantly reduced external disturbances when compared to the actual measured Rear 3-Point coordinates, while also complying with the ISO 12188 standard. These findings suggest that the proposed approach provides an effective and integrated solution for developing high-precision autonomous agricultural systems. Full article
(This article belongs to the Special Issue Soil-Machine Systems and Its Related Digital Technologies Application)
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31 pages, 8354 KiB  
Article
The Design and Experiment of a Motion Control System for the Whole-Row Reciprocating Seedling Picking Mechanism of an Automatic Transplanter
by Jiawei Shi, Jianping Hu, Wei Liu, Junpeng Lv, Yongwang Jin, Mengjiao Yao and Che Wang
Agriculture 2025, 15(13), 1423; https://doi.org/10.3390/agriculture15131423 - 30 Jun 2025
Viewed by 313
Abstract
Aiming at the problem that the whole row of reciprocating seedling picking mechanism is prone to inertial impacts during operation due to its excessive mass, causing seedling damage and positioning errors, this study builds a motion control system with a PLC controller as [...] Read more.
Aiming at the problem that the whole row of reciprocating seedling picking mechanism is prone to inertial impacts during operation due to its excessive mass, causing seedling damage and positioning errors, this study builds a motion control system with a PLC controller as the core and proposes a composite motion control strategy based on planned S-curve acceleration and deceleration and fuzzy PID to achieve rapid response, precise positioning, and smooth operation of the seedling picking mechanism. By establishing the objective function and constraint conditions and taking into account the dynamic change of the seedling picking displacement, the S-curve acceleration and deceleration control algorithm is planned in six and seven stages to meet the requirements of a smooth transition of the speed and continuous change of the acceleration curve of the seedling picking mechanism during movement. A fuzzy PID positioning control system is designed, the control system transfer function is constructed, and fuzzy rules are formulated to dynamically compensate for the error and its rate of change to meet the requirements of fast response and no overshoot oscillation of the positioning control system. The speed and acceleration of the seedling picking mechanism under the six-segment and seven-segment S-curve acceleration and deceleration motion control conditions were simulated using MATLAB2024a simulation software and compared with the trapezoidal acceleration and deceleration motion control. The planned S-curve acceleration and deceleration control algorithm has a more stable control effect on the seedling picking mechanism when it operates under the conditions of the dynamic change of the displacement, and it meets the design requirements of seedling picking efficiency. The positioning control system was modeled and simulated using the Simulink simulation platform. When KP = 15, KI = 3, and KD = 1, the whole-row seedling picking control system ran stably, responded quickly, and had no overshoot. Compared with the PID control system with fixed parameters, the fuzzy PID control system reduced the time consumption in the rising stage by 24.5% and shortened the overall stabilization process by 17.6%. The zero overshoot characteristic was ensured, and the response speed was faster. When a disturbance signal is added, the overshoot of the fuzzy PID control system is reduced by 2.4%, and the response speed is increased by 6.8% compared with the fixed-parameter PID control system. The dynamic response rate and anti-disturbance performance are better than those of the fixed-parameter PID control system. A bench comparison test was carried out. The results showed that the S-curve acceleration and deceleration motion control algorithm reduced the average mass loss rate of seedlings by 46.19% compared with the trapezoidal acceleration and deceleration motion control algorithm, and the seedling picking efficiency met the design requirements. Fuzzy PID positioning control was used, and the maximum displacement error of the end effector during seedling picking was −1.4 mm, and the average relative error rate was 0.22%, which met the positioning accuracy requirements of the end effector in the X-axis direction and verified the stability and accuracy of the designed control system. The designed control system was tested in the field, and the average comprehensive success rate of seedling picking and throwing reached 96.2%, which verified the feasibility and practicality of the control system. Full article
(This article belongs to the Special Issue Soil-Machine Systems and Its Related Digital Technologies Application)
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