Soil shear strength is an important indicator of soil erosion sensitivity and the tillage performance of the cultivated layer. Measuring soil shear strength at a field scale is difficult, time-consuming, and costly. This study proposes a new method to predict soil shear strength parameters (cohesion and internal friction angle) by combining cone penetration test (CPT) data and soil properties. A portable CPT measuring device with two pressure sensors was designed to collect two CPT data in farmland, namely cone tip resistance, and cone side pressure. Direct shear tests were performed in the laboratory to determine the soil shear strength parameters for 83 CPT data collection points. Two easily available soil properties (water content and bulk density) were determined via the oven-drying method. Using the two CPT data and the two soil properties as predictors, three machine learning (ML) models were built for predicting soil cohesion and the internal friction angle, including backpropagation neural network (BPNN), partial least squares regression (PLSR), and support vector regression (SVR). The prediction performance of each model was evaluated using the coefficient of determination (R²), the root-mean-square error (RMSE), and the relative error (RE). The results suggested that among all the evaluated models, the BPNN model was the most suitable prediction model for soil cohesion, and the SVR model performed best in predicting soil internal friction angle. Thus, our findings provide a foundation for the convenient and low-cost measurement of soil shear strength parameters.

Soil organic matter (SOM) is a key indicator of soil fertility. For accurate measurement of SOM, a novel method based on an artificial olfactory system (AOS) was proposed. The response curves of soil volatile organic compounds (VOCs) were measured using a metal-oxide semiconductor sensor array, and four features (including maximum value, mean differential coefficient, response area, and the transient value at the 20th second) were obtained from the curves and used to build olfactory feature space. Then, prediction models were established using the pattern recognition algorithm. To further enhance the accuracy of AOS measurement, we used Monte Carlo cross-validation (MCCV) to identify and eliminate the abnormal samples of the soil olfactory feature space. Then, the dimension reduction method of the genetic algorithm (GA)back-propagation (BP) was used to find the appropriate feature vectors, and two types of hybrid models were presented. One was the support vector machine (SVM) and group method of data handling (GMDH) combined model—SVM-GMDH. The other was a combination of partial least squares regression (PLSR) and back-propagation neural network (BPNN)—PLSR-BPNN. The forecasting performances of three single models (BPNN, PLSR, support vector regression: SVR) and two combined models (PLSR-BPNN, SVM-GMDH) were comparatively evaluated. The evaluation indices included coefficient of determination (R ²), root mean square error (RMSE), ratio of performance to deviation and relative prediction error (RPE). It was found that the predictive capabilities of all five tested models were improved after elimination of abnormal samples and feature reduction. Moreover, PLSR-BPNN performed the best in predicting SOM concentrations, with R ² = 0.952, RMSE = 1.771, PRD = 4.291, and slight variation of RPE within 0–0.185, and thus can offer a reference for predicting SOM via AOS.

This study is aimed to solve the parameter shortage during discrete element method (DEM) simulation in mechanized deseeding of fresh Rhizoma polygonati, and to calibrate DEM simulation parameters of fresh R. polygonati using physical measurement and simulation trials. First, the physical properties of fresh R. polygonati from picking to 15 days of stacking, and the contact parameters between fresh R. polygonati and different materials along with the prolonging of stacking days were measured through physical experiments. Based on the physical test results, the parameters significantly affecting the repose angle of fresh R. polygonati were screened out via Plackett-Burman test. The collision recovery coefficient, static friction coefficient, and rolling friction coefficient between fresh R. polygonati fruits all significantly affected the repose angle of fresh R. polygonati. On basis of Plackett-Burman test, a steepest ascent test was designed to further clarify the ranges of the significant parameters. Targeting at the actual repose angle of fresh R. olygonati (27.23°), we conducted Box-Behnken test and obtained the optimal parameter combination for DEM simulation of fresh R. polygonati. Verification tests showed the relative errors between the simulated values and measured values of repose angle in fresh R. polygonati were less than 3%, which proves the DEM simulation parameters are reliable.