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

IF 2.4 3区 工程技术 Q3 ENGINEERING, ENVIRONMENTAL Journal of Terramechanics Pub Date : 2023-09-20 DOI:10.1016/j.jterra.2023.09.002
Vijay Mahore, Peeyush Soni, Arpita Paul, Prakhar Patidar, Rajendra Machavaram
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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.

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基于机器学习的砂质粘土壤土板耕牵伸预测
开发了机器学习(ML)模型来预测在沙质粘土壤土中操作的板犁的吃水。土壤锥指数、耕作深度和作业速度对耕具的吃水有影响。我们用一个三点悬挂式测力仪来测量牵引力,用一个圆锥贯入仪来测量土壤锥指数,用旋转电位器来测量耕作深度,用接近传感器来测量工作速度。在三种不同的耕作深度和不同的运行速度下,对两底板犁的吃水要求进行了实验测量。我们使用最新的机器学习算法开发了预测模型,包括线性回归、脊回归、支持向量机、决策树、k近邻、随机森林、自适应增强、梯度增强回归、轻梯度增强机和分类增强。这些模型使用现场测量数据集进行训练和测试,包括土壤锥指数、耕作深度、运行速度和相应的牵伸值。我们将测量的草稿与常用的ASABE模型进行比较,结果R2为0.62。我们的ML模型在性能上明显优于ASABE模型。测试数据集的R2值为0.906 ~ 0.983。结果表明,所建立的机器学习模型有效地捕捉了模板犁的输入参数与牵伸之间复杂的非线性关系。
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来源期刊
Journal of Terramechanics
Journal of Terramechanics 工程技术-工程:环境
CiteScore
5.90
自引率
8.30%
发文量
33
审稿时长
15.3 weeks
期刊介绍: The Journal of Terramechanics is primarily devoted to scientific articles concerned with research, design, and equipment utilization in the field of terramechanics. The Journal of Terramechanics is the leading international journal serving the multidisciplinary global off-road vehicle and soil working machinery industries, and related user community, governmental agencies and universities. The Journal of Terramechanics provides a forum for those involved in research, development, design, innovation, testing, application and utilization of off-road vehicles and soil working machinery, and their sub-systems and components. The Journal presents a cross-section of technical papers, reviews, comments and discussions, and serves as a medium for recording recent progress in the field.
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