Machine learning approach for predicting soil penetration resistance under different moisture conditions

IF 2.4 3区 工程技术 Q3 ENGINEERING, ENVIRONMENTAL Journal of Terramechanics Pub Date : 2023-12-01 DOI:10.1016/j.jterra.2023.08.002
Anis Elaoud , Hanen Ben Hassen , Rim Jalel , Nahla Ben Salah , Afif Masmoudi , Atef Masmoudi
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不同湿度条件下土壤渗透阻力预测的机器学习方法
对重复设备(拖拉机、耕耘机等)通过后土壤压实现象的调查和评估被认为是保护农业土壤免受退化和维持可持续农业的预防性解决方案。事实上,目前还没有可靠的方法来预测土壤压实的主要原因,特别是在潮湿的土壤中。本研究应用人工神经网络(ANN)模型进行阻力渗透预测。使用从测量的实验值获得的阻力穿透(Rp)测试数据来训练模型。学习得分系数(0.96)、RMSE(0.51)和MAE(0.39)表明,ANN模型的预测与实测现场数据一致。结果表明,所建立的人工神经网络模型可以有效地预测不同水分条件下的土壤压实状态。这项工作将帮助农民优化机器使用,从而以最低成本提高产量。
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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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