农机化研究2025，Vol.47Issue(1)：7-15,9.DOI:10.13427/j.issn.1003-188X.2025.01.002
基于机器学习的农田土壤抗剪强度参数检测方法研究
Research on the Detection Method of Farmland Soil Shear Strength Parameters Based on Machine Learning
摘要
Abstract
Soil shear strength parameters,including cohesion and internal friction angle,are important indexes to evalu-ate soil erosion sensitivity and reflect tillage performance in topsoil.In order to realize the rapid detection of soil shear strength parameters,a method of soil shear strength parameters detection based on machine learning was proposed.With an STM32 micro-controller as the core processor,the soil data acquisition device was constructed with cone rod,ball screw sliding table and triangle bracket.A DYMH-103 column pressure sensor and a FlexiForce film sensor were used to detect cone tip resistance and cone lateral pressure of cone rod penetration into soil respectively,and a CSF11 soil mois-ture sensor was used to obtain soil moisture content information.The feature vectors were extracted from the data collected by these sensors to construct the modeling data set.Then the Monte Carlo Cross Validation(MCCV)was used to elimi-nate 4 abnormal samples from the dataset.Furthermore,an ELM-PLSR combined modeling algorithm was proposed.Finally,three different machine learning models,ELM,PLSR and ELM-PLSR,were compared and evaluated using the determination coefficient R2 and RPD as evaluation indexes.The results show that the predictive performance of the ELM-PLSR model is better than that of the ELM model and the PLSR model.The R2 and RPD of ELM-PLSR to the co-hesion detection are 0.919 and 3.475,and to the internal friction angle detection are 0.910 and 3.304,respectively.This study can provide a reference method for the rapid measurement of soil shear strength parameters.关键词
机器学习/土壤抗剪强度/多传感器/特征向量/预测模型Key words
machine learning/soil shear strength/multiple sensor/feature vector/prediction model分类
农业科学引用本文复制引用
于艳艳,朱龙图,刘鹤..基于机器学习的农田土壤抗剪强度参数检测方法研究[J].农机化研究,2025,47(1):7-15,9.基金项目
吉林省科技厅中青年科技创新创业卓越人才(团队)项目(20220508133RC) （团队）
中国博士后科学基金项目(2021M701341) （2021M701341）
