Vijay Mahore, Peeyush Soni, Arpita Paul, Prakhar Patidar, Rajendra Machavaram
Journal of Terramechanics, Volume 111, 2024, Pages 31-40, ISSN 0022-4898
https://doi.org/10.1016/j.jterra.2023.09.002.(https://www.sciencedirect.com/science/article/pii/S0022489823000836)
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.
Keywords: Draft prediction; Machine Learning; Mouldboard ploughing; Gradient Boosting; Random Forest; Tillage operation