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Agriculture
Special Issues
Soil–Machine Systems and Related Farming Machinery
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Soil–Machine Systems and Related Farming Machinery

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

Deadline for manuscript submissions: closed (25 November 2024) | Viewed by 3442

Special Issue Editors

Dr. Ju-Seok Nam

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Kangwon National University, 1 Kangwondaehak-gil, 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. Yongjin Cho

E-Mail Website
Guest Editor
Department of Bioindustrial Machinery Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
Interests: agriculture machinery; precision agriculture; soil and crop sensing; remote monitoring system; VIS–NIR spectroscopy; online soil measurement
Special Issues, Collections and Topics in MDPI journals
Dr. Yeon-Soo Kim

E-Mail Website
Guest Editor
Department of Bio-Industrial Machinery Engineering, Pusan National University, Miryang 50463, Republic of Korea
Interests: agricultural mechanization; off-road machinery system; digital twin modeling; agricultural digitization; soil tillage

Special Issue Information

Dear Colleagues,

The mechanization of agricultural works has greatly contributed to the improvement of agricultural productivity and a 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 the 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 soil; hence, it should be designed in consideration of its interaction with soil. It is possible to optimally design agricultural machinery by understanding both the characteristics of the soil concerned and the characteristics of the mechanical system.

This Special Issue is a natural continuation of our previous Special Issue, “Soil Mechanical Systems and Related Farming Machinery“’, and will focus on research regarding soil–machine systems in agriculture, including design, analysis, experimentation, etc. In addition to soil-related research, agricultural machinery- and automation-related research is also of interest. This also includes off-road environments as well as greenhouse or smart farm applications. Both original research articles and comprehensive reviews are welcome.

Dr. Ju-Seok Nam
Dr. Yongjin Cho
Dr. Yeon-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. Agriculture 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 engineering
  • agricultural machinery
  • biosystem engineering
  • off-road farming
  • smart farming
  • soil–machine systems
  • precision agriculture
  • soil and crop

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Published Papers (3 papers)

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Research

16 pages, 3277 KiB  
Article
Proximal Absorbance Calibration Method Using an Embedded Blank Reference RGB Sensor for Determination of Ion Concentrations
by Jung-Kyu Lee, Ye-Hun Lee and Dong-Hoon Lee
Agriculture 2024, 14(12), 2171; https://doi.org/10.3390/agriculture14122171 - 28 Nov 2024
Viewed by 313
Abstract
Accurate analyses and management of ion concentrations are crucial in precision agriculture. Modern technology-based methods are non-destructive and do not require sample preparation, enabling fast and accurate analysis; however, they have limitations when processing multiple samples. In this study, a multi-ion analysis system [...] Read more.
Accurate analyses and management of ion concentrations are crucial in precision agriculture. Modern technology-based methods are non-destructive and do not require sample preparation, enabling fast and accurate analysis; however, they have limitations when processing multiple samples. In this study, a multi-ion analysis system was developed for the prompt and accurate analysis of concentrations of important ions such as NO3−, HnPO4−, K+, Ca2+, and Mg2+. The RGB sensitivity control was automated through calibrations by applying a reference slot-based error rate across six slots between sample measurements, facilitating sample-to-sample comparisons and enabling accurate concentration analysis. By analyzing the correlation between each ion concentration and the proximal absorbance-based concentration prediction in the simultaneous analysis system, the accuracy was verified by achieving a coefficient of determination exceeding 0.99 for most ions. This system minimizes possible deviations between slots by using an automatic calibration algorithm, thereby facilitating the simultaneous analysis of multiple samples. This is important for saving time and cost and can help in real-time nutrient analysis and monitoring in agriculture. Full article
(This article belongs to the Special Issue Soil–Machine Systems and Related Farming Machinery)
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20 pages, 5258 KiB  
Article
Classification of Garlic (Allium sativum L.) Crops by Fertilizer Differences Using Ground-Based Hyperspectral Imaging System
by Hwanjo Chung, Seunghwan Wi, Byoung-Kwan Cho and Hoonsoo Lee
Agriculture 2024, 14(8), 1215; https://doi.org/10.3390/agriculture14081215 - 24 Jul 2024
Cited by 1 | Viewed by 1081
Abstract
In contemporary agriculture, enhancing the efficient production of crops and optimizing resource utilization have become paramount objectives. Garlic growth and quality are influenced by various factors, with fertilizers playing a pivotal role in shaping both aspects. This study aimed to develop classification models [...] Read more.
In contemporary agriculture, enhancing the efficient production of crops and optimizing resource utilization have become paramount objectives. Garlic growth and quality are influenced by various factors, with fertilizers playing a pivotal role in shaping both aspects. This study aimed to develop classification models for distinguishing garlic fertilizer application differences by employing statistical and machine learning techniques, such as partial least squares (PLS), based on data acquired from a ground-based hyperspectral imaging system in the agricultural sector. The garlic variety chosen for this study was Hongsan, and the fertilizer application plots were segmented into three distinct sections. Data were acquired within the VIS/NIR wavelength range using hyperspectral imaging. Following data acquisition, the standard normal variate (SNV) pre-processing technique was applied to enhance the dataset. To identify the optimal wavelengths, various techniques such as sequential forward selection (SFS), successive projections algorithm (SPA), variable importance in projection (VIP), and interval partial least squares (iPLS) were employed, resulting in the selection of 12 optimal wavelengths. For the fertilizer application difference model, six integrated vegetation indices were chosen for comparison with existing growth indicators. Using the same methodology, the model construction showed accuracies of 90.7% for PLS. Thus, the proposed model suggests that efficient regulation of garlic fertilizer application can be achieved by utilizing statistical and machine learning techniques. Full article
(This article belongs to the Special Issue Soil–Machine Systems and Related Farming Machinery)
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17 pages, 6902 KiB  
Article
Analyzing Safety Factors and Predicting Fatigue Life of Weak Points in an Electrically Driven, Multi-Purpose Cultivation Tractor
by In-Seok Hwang, Jeong-Hun Kim, Wan-Tae Im, Hwan-Hong Jeung, Ju-Seok Nam and Chang-Seop Shin
Agriculture 2024, 14(3), 416; https://doi.org/10.3390/agriculture14030416 - 5 Mar 2024
Viewed by 1093
Abstract
The advancement of agriculture and a shortage of labor have led to an increased use of agricultural machinery. However, the resulting environmental issues have prompted a shift from internal combustion engines to electric drivetrains. The electric drivetrain includes the installation of batteries, which [...] Read more.
The advancement of agriculture and a shortage of labor have led to an increased use of agricultural machinery. However, the resulting environmental issues have prompted a shift from internal combustion engines to electric drivetrains. The electric drivetrain includes the installation of batteries, which can lead to decreased energy efficiency and significant loads on the vehicle due to their heavy weight. Consequently, the importance of ensuring the safety of agricultural machinery is being increasingly emphasized. The load on the frame of agricultural machinery is not consistent during off-road driving, and the accumulation of load cycles can lead to the destruction and failure of components. Therefore, it is necessary to ensure a level of safety and to predict the fatigue life. In this study, we estimate the safety factor and predict the fatigue life of weak points in an electrically driven, multi-purpose cultivation tractor based on working conditions (width, soil, and drive). Strain gauges were attached to these weak points to measure the strain, which was then converted to von Mises stress. Fatigue life was predicted using the rainflow counting method and the Palmgren–Miner rule. The results showed that the safety factor measured under various working conditions was greater than 1. The estimated minimum fatigue life was 124,176 years. Considering that the cultivator is used for 29.7 h annually and has a durability lifespan of 5 years, it is expected to be safely usable throughout its service life. Full article
(This article belongs to the Special Issue Soil–Machine Systems and Related Farming Machinery)
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