IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i12p2075-d992494.html
   My bibliography  Save this article

Evaluation of Agricultural Machinery Operational Benefits Based on Semi-Supervised Learning

Author

Listed:
  • Yashuo Li

    (State Key Laboratory of Soil Plant Machine System Technology, China Academy of Agricultural Mechanization Science Group Co., Ltd., Beijing 100083, China)

  • Bo Zhao

    (State Key Laboratory of Soil Plant Machine System Technology, China Academy of Agricultural Mechanization Science Group Co., Ltd., Beijing 100083, China)

  • Weipeng Zhang

    (State Key Laboratory of Soil Plant Machine System Technology, China Academy of Agricultural Mechanization Science Group Co., Ltd., Beijing 100083, China)

  • Liguo Wei

    (State Key Laboratory of Soil Plant Machine System Technology, China Academy of Agricultural Mechanization Science Group Co., Ltd., Beijing 100083, China)

  • Liming Zhou

    (State Key Laboratory of Soil Plant Machine System Technology, China Academy of Agricultural Mechanization Science Group Co., Ltd., Beijing 100083, China)

Abstract

Judging the efficiency of agricultural machinery operations is the basis for evaluating the utilization rate of agricultural machinery, the driving abilities of operators, and the effectiveness of agricultural machinery management. A range of evaluative factors—including operational efficiency, oil consumption, operation quality, repetitive operation rate, and the proportion of effective operation time—must be considered for a comprehensive evaluation of the quality of a given operation, an analysis of the causes of impact, the improvement of agricultural machinery management and an increase in operational efficiency. In this study, the main factors affecting the evaluation of agricultural machinery operations are extracted, and information about the daily operations of particular items of agricultural machinery is taken as a data source. As regards modeling, a subset of data can be scored manually, and the remaining data is predicted after the training of the relevant model. With a large quantity of data, manual scoring is not only time-consuming and labor-intensive, but also produces sample errors due to subjective factors. However, a small number of samples cannot support an accurate evaluation model, and so in this study a semi-supervised learning method was used to increase the number of training samples and improve the accuracy of the least-squares support vector machine (LSSVM) training model. The experiment used 33,000 deep subsoiling operation data, 500 of which were used as training samples and 500 as test samples. The accuracy rate of the model obtained using 500 training samples was 94.43%, and the accuracy rate achieved with this method with an increased number of training samples was 96.83%. An optimal combination of agricultural machinery and tools is recommended owing to their operational benefits in terms of reduced costs and improved operating capacity.

Suggested Citation

  • Yashuo Li & Bo Zhao & Weipeng Zhang & Liguo Wei & Liming Zhou, 2022. "Evaluation of Agricultural Machinery Operational Benefits Based on Semi-Supervised Learning," Agriculture, MDPI, vol. 12(12), pages 1-17, December.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:2075-:d:992494
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/12/2075/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/12/2075/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaokai Guo & Xianguo Yan & Zhi Chen & Zhiyu Meng, 2021. "A Novel Closed-Loop System for Vehicle Speed Prediction Based on APSO LSSVM and BP NN," Energies, MDPI, vol. 15(1), pages 1-19, December.
    2. Mohammad Askari & Yousef Abbaspour-Gilandeh & Ebrahim Taghinezhad & Ahmed Mohamed El Shal & Rashad Hegazy & Mahmoud Okasha, 2021. "Applying the Response Surface Methodology (RSM) Approach to Predict the Tractive Performance of an Agricultural Tractor during Semi-Deep Tillage," Agriculture, MDPI, vol. 11(11), pages 1-14, October.
    3. Wang, Yahui & Li, Xiubin & Lu, Dan & Yan, Jianzhong, 2020. "Evaluating the impact of land fragmentation on the cost of agricultural operation in the southwest mountainous areas of China," Land Use Policy, Elsevier, vol. 99(C).
    4. Zhenxing Zhao & Kaijie Chen & Ying Chen & Yuxing Dai & Zeng Liu & Kuiyin Zhao & Huan Wang & Zishun Peng, 2021. "An Ultra-Fast Power Prediction Method Based on Simplified LSSVM Hyperparameters Optimization for PV Power Smoothing," Energies, MDPI, vol. 14(18), pages 1-15, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Orji J. E. & Igwe C. A. & Ezeaku P. I. & Osuji E. E. & Iroegbu C. S. & Emeka C. P. O. & Chinaka I. C. & Dada O. & Ankrumah E. & Osang E. A. & Amenkhienan B. E., 2023. "Variation in Soil Physicochemical Properties of Different Land Use Types in Abakaliki, South Eastern Nigeria," Journal of Agriculture and Crops, Academic Research Publishing Group, vol. 9(4), pages 462-471, 10-2023.
    2. Guangwei Wu & Shoujiang Wang & Anqi Zhang & Yuejin Xiao & Liwei Li & Yanxin Yin & Hanqing Li & Changkai Wen & Bingxin Yan, 2023. "Optimized Design and Experiment of a Self-Covering Furrow Opener for an Automatic Sweet Potato Seedling Transplanting Machine," Sustainability, MDPI, vol. 15(17), pages 1-19, August.
    3. Xiuling Ding & Qian Lu & Lipeng Li & Apurbo Sarkar & Hua Li, 2023. "Does Labor Transfer Improve Farmers’ Willingness to Withdraw from Farming?—A Bivariate Probit Modeling Approach," Land, MDPI, vol. 12(8), pages 1-27, August.
    4. Yishao Shi & Qianqian Yang & Liangliang Zhou & Shouzheng Shi, 2022. "Can Moderate Agricultural Scale Operations Be Developed against the Background of Plot Fragmentation and Land Dispersion? Evidence from the Suburbs of Shanghai," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    5. Jinna Yu & Yiming Wei & Wei Fang & Zhen Liu & Yujie Zhang & Jing Lan, 2021. "New Round of Collective Forest Rights Reform, Forestland Transfer and Household Production Efficiency," Land, MDPI, vol. 10(9), pages 1-22, September.
    6. Meseret Abatechanie & Baozhong Cai & Fang Shi & Yuanji Huang, 2022. "The Environmental and Socio-Economic Effect of Farmland Management Right Transfer in China: A Systematic Review," Land, MDPI, vol. 11(8), pages 1-21, August.
    7. Xilong Jiang & Guanyi Yin & Yi Lou & Shuai Xie & Wei Wei, 2021. "The Impact of Transformation of Farmers’ Livelihood on the Increasing Labor Costs of Grain Plantation in China," Sustainability, MDPI, vol. 13(21), pages 1-18, October.
    8. Aoxi Yang & Yahui Wang, 2022. "Spatiotemporal Variations in Farmland Rents and Its Drivers in Rural China: Evidence from Plot-Level Transactions," Land, MDPI, vol. 11(2), pages 1-14, February.
    9. Meng Wang & Qingchen Xu & Zemeng Fan & Xiaofang Sun, 2021. "The Imprint of Built-Up Land Expansion on Cropland Distribution and Productivity in Shandong Province," Land, MDPI, vol. 10(6), pages 1-12, June.
    10. Shukun Wang & Dengwang Li & Tingting Li & Changquan Liu, 2021. "Land Use Transitions and Farm Performance in China: A Perspective of Land Fragmentation," Land, MDPI, vol. 10(8), pages 1-22, July.
    11. Jie Zhang & Meiqiu Chen & Chang Huang & Zhaohao Lai, 2022. "Labor Endowment, Cultivated Land Fragmentation, and Ecological Farming Adoption Strategies among Farmers in Jiangxi Province, China," Land, MDPI, vol. 11(5), pages 1-17, May.
    12. Guanyi Yin & Yi Lou & Shuai Xie & Wei Wei, 2022. "Evaluation of the Response of Grain Productivity to Different Arable Land Allocation Intensities in the Land Use Planning System of China," Sustainability, MDPI, vol. 14(5), pages 1-20, March.
    13. Fraz Ahmad Khan & Abdul Ghafoor & Muhammad Azam Khan & Muhammad Umer Chattha & Farzaneh Khorsandi Kouhanestani, 2022. "Parameter Optimization of Newly Developed Self-Propelled Variable Height Crop Sprayer Using Response Surface Methodology (RSM) Approach," Agriculture, MDPI, vol. 12(3), pages 1-19, March.
    14. Bo Li & Hao Ouyang & Tong Wang & Tian Dong, 2023. "Coupling Relationship between Rural Settlement Patterns and Landscape Fragmentation in Woodlands and Biological Reserves—A Case of Nanshan National Park," Land, MDPI, vol. 12(4), pages 1-25, March.
    15. Tianxiao Zhou & Zhiwen Luo & Xiaobin Zhang, 2024. "How do China's villages self‐organize collective land use under the background of rural revitalization? A multi‐case study in Zhejiang, Fujian and Guizhou provinces," Growth and Change, Wiley Blackwell, vol. 55(1), March.
    16. Nguyen Thai Phan & Ji-Yong Lee & Nguyen Duc Kien, 2022. "The Impact of Land Fragmentation in Rice Production on Household Food Insecurity in Vietnam," Sustainability, MDPI, vol. 14(18), pages 1-12, September.
    17. Mingyan Ni & Yindi Zhao & Caihong Ma & Wenzhi Jiang & Yanmei Xie & Xiaolin Hou, 2023. "Spatial Identification and Change Analysis of Production-Living-Ecological Space Using Multi-Source Geospatial Data: A Case Study in Jiaodong Peninsula, China," Land, MDPI, vol. 12(9), pages 1-27, September.
    18. Guanglian Luo & Bin Wang & Ruiwei Li & Dongqi Luo & Chaofu Wei, 2022. "Study of the Agglomeration Characteristics of Cultivated Land in Underdeveloped Mountainous Areas Based on Spatial Auto-Correlation: A Case of Pengshui County, Chongqing, China," Land, MDPI, vol. 11(6), pages 1-14, June.
    19. Jose Cruz & Christian Romero & Oscar Vera & Saul Huaquipaco & Norman Beltran & Wilson Mamani, 2023. "Multiparameter Regression of a Photovoltaic System by Applying Hybrid Methods with Variable Selection and Stacking Ensembles under Extreme Conditions of Altitudes Higher than 3800 Meters above Sea Lev," Energies, MDPI, vol. 16(12), pages 1-21, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:2075-:d:992494. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.