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Agriculture
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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: 31 August 2026 | Viewed by 6420

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 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

  • 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 (6 papers)

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Research

28 pages, 7516 KB  
Article
GAE-SpikeYOLO: An Energy-Efficient Tea Bud Detection Model with Spiking Neural Networks for Complex Natural Environments
by Junhao Liu, Jiaguo Jiang, Haomin Liang, Guanquan Zhu, Minyi Ye, Hongyu Chen, Yonglin Chen, Anqi Cheng, Ruiming Sun and Yubin Zhong
Agriculture 2026, 16(3), 353; https://doi.org/10.3390/agriculture16030353 - 1 Feb 2026
Viewed by 741
Abstract
Tea bud recognition and localization constitute a fundamental step toward enabling fine-grained tea plantation management and intelligent harvesting, offering substantial value in improving the picking quality of premium tea materials, reducing labor dependency, and accelerating the development of smart tea agriculture. However, most [...] Read more.
Tea bud recognition and localization constitute a fundamental step toward enabling fine-grained tea plantation management and intelligent harvesting, offering substantial value in improving the picking quality of premium tea materials, reducing labor dependency, and accelerating the development of smart tea agriculture. However, most existing methods for detecting tea buds are built upon Artificial Neural Networks (ANNs) and rely extensively on floating-point computation, making them difficult to deploy efficiently on energy-constrained edge platforms. To address this challenge, this paper proposes an energy-efficient tea bud detection model, GAE-SpikeYOLO, which improves upon the Spiking Neural Networks (SNNs) detection framework SpikeYOLO. Firstly, Gated Attention Coding (GAC) is introduced into the input encoding stage to generate spike streams with richer spatiotemporal dynamics, strengthening shallow feature saliency while suppressing redundant background spikes. Secondly, the model incorporates the Temporal-Channel-Spatial Attention (TCSA) module into the neck network to enhance deep semantic attention on tea bud regions and effectively suppress high-level feature responses unrelated to the target. Lastly, the proposed model adopts the EIoU loss function to further improve bounding box regression accuracy. The detection capability of the model is systematically validated on a tea bud object detection dataset collected in natural tea garden environments. Experimental results show that the proposed GAE-SpikeYOLO achieves a Precision (P) of 83.0%, a Recall (R) of 72.1%, a mAP@0.5 of 81.0%, and a mAP@[0.5:0.95] of 60.4%, with an inference energy consumption of only 49.4 mJ. Compared with the original SpikeYOLO, the proposed model improves P, R, mAP@0.5, and mAP@[0.5:0.95] by 1.4%, 1.6%, 2.0%, and 3.3%, respectively, while achieving a relative reduction of 24.3% in inference energy consumption. The results indicate that GAE-SpikeYOLO provides an efficient and readily deployable solution for tea bud detection and other agricultural vision tasks in energy-limited scenarios. Full article
(This article belongs to the Special Issue Soil-Machine Systems and Its Related Digital Technologies Application)
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28 pages, 29078 KB  
Article
Field Performance and Wear Behavior of Atmospheric Plasma Spraying (APS) Coated Discs Used in Agricultural Disc Harrows
by Vlad Nicolae Arsenoaia, Corneliu Munteanu, Fabian Cezar Lupu, Bogdan Istrate, Marcelin Benchea and Iurie Melnic
Agriculture 2026, 16(1), 114; https://doi.org/10.3390/agriculture16010114 - 1 Jan 2026
Cited by 2 | Viewed by 504
Abstract
The wear performance of coated and uncoated harrow discs was evaluated under real agricultural field conditions in order to assess the long-term effectiveness of three atmospheric plasma spraying (APS) systems: a Cr2O3–SiO2–TiO2 ceramic coating, a WC/W [...] Read more.
The wear performance of coated and uncoated harrow discs was evaluated under real agricultural field conditions in order to assess the long-term effectiveness of three atmospheric plasma spraying (APS) systems: a Cr2O3–SiO2–TiO2 ceramic coating, a WC/W2C–Co carbide coating, and a Co–Cr–Ni–W–C alloy coating. In contrast to most previous studies focused on laboratory testing or short-term trials, the present work provides a comparative long-term field evaluation over 50 ha per disc (1000 ha total) under identical operating conditions in quartz-rich Argic Luvisol soil. Disc wear was quantified through periodic mass-loss and diameter measurements, complemented by microstructural and SEM analyses. The uncoated disc exhibited the most severe degradation, with a total mass loss of approximately 700 g and rapid acceleration of wear after the first 5–10 ha. The ceramic-coated disc showed the highest durability, limiting mass loss to approximately 390 g, corresponding to a reduction of about 44%, and maintaining the largest residual diameter after field operation. The Co-based alloy provided intermediate performance (~16% mass-loss reduction), while the carbide coating showed limited improvement (~7% reduction) due to microcracking and weak carbide–binder interfaces. The results demonstrate that, under real field conditions, coating microstructural integrity is more critical than nominal hardness, and highlight the superior effectiveness of ceramic APS coatings for extending disc service life in abrasive agricultural soils. Full article
(This article belongs to the Special Issue Soil-Machine Systems and Its Related Digital Technologies Application)
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21 pages, 1755 KB  
Article
Analysis on Economic Improvement Based on Energy Efficiency of Agricultural Tractors in South Korea During a Decade
by Wan-Tae Im, In-Seok Hwang, Moon-Kyung Jang, Jung-Hoon Kim, Tae-Ho Han, Young-Tae Kim, Youn-Koo Kang, Ju-Seok Nam and Chang-Seop Shin
Agriculture 2025, 15(24), 2598; https://doi.org/10.3390/agriculture15242598 - 16 Dec 2025
Viewed by 668
Abstract
In recent years, the rapidly changing environment and climate have emphasized the need for sustainable development, particularly in the agricultural sector. Tractors are the most widely used machines in agriculture, making their energy efficiency crucial not only for environmental protection but also for [...] Read more.
In recent years, the rapidly changing environment and climate have emphasized the need for sustainable development, particularly in the agricultural sector. Tractors are the most widely used machines in agriculture, making their energy efficiency crucial not only for environmental protection but also for reducing farming costs and enhancing economic sustainability. This study applies Yeo–Johnson data transformation to normalize the discretized data of 111 tractor models, enabling the classification of agricultural tractors based on energy efficiency. Tractors were categorized into five classes according to energy efficiency, and the upper limit of each class was used to quantify the rate of improvement in energy efficiency. Furthermore, a comparative analysis between the classification model from 2006 to 2010 and that from 2016 to 2020 demonstrated that the latter exhibits superior energy consumption efficiency. Specifically, the 2016–2020 model showed an improvement in energy efficiency ranging from approximately 20.57% to 54.86% across all power categories, with higher-rated power tractors achieving greater improvements. This comparison confirms that the energy efficiency of tractors in the latest classification model is further improved, reflecting the substantial technological advancements made over the past decade. Full article
(This article belongs to the Special Issue Soil-Machine Systems and Its Related Digital Technologies Application)
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24 pages, 18138 KB  
Article
Image-Based Interpolation of Soil Surface Imagery for Estimating Soil Water Content
by Eunji Jung, Dongseok Kim, Jisu Song and Jaesung Park
Agriculture 2025, 15(17), 1812; https://doi.org/10.3390/agriculture15171812 - 25 Aug 2025
Cited by 1 | Viewed by 1169
Abstract
Soil water content (SWC) critically governs the physical and mechanical behavior of soils. However, conventional methods such as oven drying are laborious, time-consuming, and difficult to replicate in the field. To overcome these limitations, we developed an image-based interpolation framework that leverages histogram [...] Read more.
Soil water content (SWC) critically governs the physical and mechanical behavior of soils. However, conventional methods such as oven drying are laborious, time-consuming, and difficult to replicate in the field. To overcome these limitations, we developed an image-based interpolation framework that leverages histogram statistics from 12 soil surface photographs spanning 3.83% to 19.75% SWC under controlled lighting. For each image, pixel-level values of red, green, blue (RGB) channels and hue, saturation, value (HSV) channels were extracted to compute per-channel histograms, whose empirical means and standard deviations were used to parameterize Gaussian probability density functions. Linear interpolation of these parameters yielded synthetic histograms and corresponding images at 1% SWC increments across the 4–19% range. Validation against the original dataset, using dice score (DS), Bhattacharyya distance (BD), and Earth Mover’s Distance (EMD) metrics, demonstrated that the interpolated images closely matched observed color distributions. Average BD was below 0.014, DS above 0.885, and EMD below 0.015 for RGB channels. For HSV channels, average BD was below 0.074, DS above 0.746, and EMD below 0.022. These results indicate that the proposed method reliably generates intermediate SWC data without additional direct measurements, especially with RGB. By reducing reliance on exhaustive sampling and offering a cost-effective dataset augmentation, this approach facilitates large-scale, noninvasive soil moisture estimation and supports machine learning applications where field data are scarce. Full article
(This article belongs to the Special Issue Soil-Machine Systems and Its Related Digital Technologies Application)
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22 pages, 3432 KB  
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 1332
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 KB  
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
Cited by 2 | Viewed by 1188
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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