基于机器学习的农田土壤抗剪强度参数检测方法研究OA北大核心

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.