Highlights Machine learning-based draft prediction models for mouldboard ploughs in sandy clay loam soil are developed. ML models effectively captured complex relationship between input parameters and draft, outperforming the traditional ASABE model. The findings highlight the potential of ML models to manage tillage operations in sandy clay loam soil through more accurate draft prediction.

    Abstract Machine learning (ML) models are developed to predict draft for mouldboard ploughs operating in sandy-clay-loam soil. The draft of tillage tools is influenced by soil cone-index, tillage-depth, and operating-speed. We used a three-point hitch dynamometer to measure draft force, a cone penetrometer for soil cone-index, rotary potentiometers for tillage-depth, and proximity sensors for operating-speed. Draft requirements were experimentally measured for a two-bottom mouldboard plough at three different tillage-depths and various operating-speeds. We developed prediction models using recent ML algorithms, including Linear-Regression, Ridge-Regression, Support-Vector-Machines, Decision-Trees, k-Nearest-Neighbours, Random-Forests, Adaptive-Boosting, Gradient-Boosting-Regression, Light-Gradient-Boosting-Machine, and Categorical-Boosting. These models were trained and tested using a dataset of field measurements including soil cone-index, tillage-depth, operating-speed, and corresponding draft values. We compared the measured draft with the commonly used ASABE model, which resulted in an R2 of 0.62. Our ML models outperformed the ASABE model with significantly better performance. The test data set achieved R2 values ranging from 0.906 to 0.983. These results demonstrate that the developed ML models effectively capture the complex nonlinear relationship between input parameters and draft of mouldboard plough.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Machine learning-based draft prediction for mouldboard ploughing in sandy clay loam soil


    Contributors:

    Published in:

    Publication date :

    2023-09-11


    Size :

    10 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Traction prediction on a sandy loam soil for a single wheel tester

    Keen, A. / International Society for Terrain Vehicle Systems | British Library Conference Proceedings | 1998


    Mouldboard surfaces design in China

    Yuan, S. Y. / International Society for Terrain Vehicle Systems | British Library Conference Proceedings | 1988


    Effect of animal drawn puddling implements on hydraulic conductivity of lateritic sandy clay loam soil in West Bengal, India

    Shrivastava,A.K. / Datta,R.K. / College of Agricultural Engng.,IN et al. | Automotive engineering | 2006