Abstract
Data detailed on Machine Learning have been presented. According to news reporting from West Bengal, India, by NewsRx journalists, research stated, "Machine learning (ML) models are developed to predict draft for mouldboard ploughs operating in sandyclay-loam soil. The draft of tillage tools is influenced by soil cone-index, tillage-depth, and operatingspeed." The news correspondents obtained a quote from the research from the Indian Institute of Technology (IIT) Kharagpur, "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, includ- ing Linear-Regression, Ridge-Regression, Support-Vector-Machines, Decision-Trees, k-Nearest-Neighbours, Random-Forests, Adaptive-Boosting, Gradient-Boosting-Regression, LightGradient-Boosting-Machine, and Categorical-Boosting. These models were trained and tested using a dataset of field measurements includ- ing 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."
NETL
NSTL
Elsevier