IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2023i1p47-d1308209.html
   My bibliography  Save this article

Using Models and Artificial Neural Networks to Predict Soil Compaction Based on Textural Properties of Soils under Agriculture

Author

Listed:
  • Hamza Negiş

    (Department of Soil Science and Nutrition, Faculty of Agriculture, Selcuk University, Konya 42130, Turkey)

Abstract

This study focuses on addressing the challenges associated with labor-intensive soil penetration resistance (SPR) measurements, which are prone to errors due to varying soil moisture levels. The innovative approach involves developing SPR estimation models using artificial neural networks (ANN) for soils with optimal moisture levels determined by van Genuchten (WG) calculations. Sampling and measurements were conducted at 280 points (0–30 cm depth), with an additional 324 samples used for model testing. Considering six scenarios, this study aimed to identify the best estimation model using key soil properties (sand, clay, silt, bulk density, organic carbon, and aggregate stability) in different combinations affecting SPR. Results from all ANN scenarios demonstrated satisfactory SPR estimation performance, with the sand and clay content scenario exhibiting the highest accuracy, characterized by a mean square error (MSE) of 0.0029 and a coefficient of determination (R 2 ) value of 0.9707. This selected scenario were further validated with different test data, yielding an MSE of 0.7891 and an R 2 value of 0.67. In conclusion, this study suggests that, by standardizing moisture levels through WG calculations, ANN-based SPR estimation can effectively be applied to soils with specific sand and clay contents.

Suggested Citation

  • Hamza Negiş, 2023. "Using Models and Artificial Neural Networks to Predict Soil Compaction Based on Textural Properties of Soils under Agriculture," Agriculture, MDPI, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:gam:jagris:v:14:y:2023:i:1:p:47-:d:1308209
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/1/47/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/1/47/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peipei Yang & Wenxu Dong & Marius Heinen & Wei Qin & Oene Oenema, 2022. "Soil Compaction Prevention, Amelioration and Alleviation Measures Are Effective in Mechanized and Smallholder Agriculture: A Meta-Analysis," Land, MDPI, vol. 11(5), pages 1-18, April.
    2. Yamaç, Sevim Seda & Şeker, Cevdet & Negiş, Hamza, 2020. "Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area," Agricultural Water Management, Elsevier, vol. 234(C).
    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. Monika Vilkiene & Ieva Mockeviciene & Grazina Kadziene & Danute Karcauskiene & Regina Repsiene & Ona Auskalniene, 2023. "Bacterial Communities: Interaction to Abiotic Conditions under Effect of Anthropogenic Pressure," Sustainability, MDPI, vol. 15(14), pages 1-15, July.
    2. Yamaç, Sevim Seda, 2021. "Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area," Agricultural Water Management, Elsevier, vol. 254(C).
    3. Felicia Cheţan & Teodor Rusu & Roxana Elena Călugăr & Cornel Chețan & Alina Şimon & Adrian Ceclan & Marius Bărdaș & Olimpia Smaranda Mintaș, 2022. "Research on the Interdependence Linkages between Soil Tillage Systems and Climate Factors on Maize Crop," Land, MDPI, vol. 11(10), pages 1-14, October.
    4. Priya Brata Bhoi & Veeresh S. Wali & Deepak Kumar Swain & Kalpana Sharma & Akash Kumar Bhoi & Manlio Bacco & Paolo Barsocchi, 2021. "Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach," Agriculture, MDPI, vol. 11(9), pages 1-27, August.

    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:14:y:2023:i:1:p:47-:d:1308209. 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.