Soil organic matter (SOM) is a major indicator of soil fertility and nutrients. In this study, a soil organic matter measuring method based on an artificial olfactory system (AOS) was designed. An array composed of 10 identical gas sensors controlled at different temperatures was used to collect soil gases. From the response curve of each sensor, four features were extracted (maximum value, mean differential coefficient value, response area value, and the transient value at the 20th second). Then, soil organic matter regression prediction models were built based on back-propagation neural network (BPNN), support vector regression (SVR), and partial least squares regression (PLSR). The prediction performance of each model was evaluated using the coefficient of determination (R2), root-mean-square error (RMSE), and the ratio of performance to deviation (RPD). It was found that the R2 values between prediction (from BPNN, SVR, and PLSR) and observation were 0.880, 0.895, and 0.808. RMSEs were 14.916, 14.094, and 18.890, and RPDs were 2.837, 3.003, and 2.240, respectively. SVR had higher prediction ability than BPNN and PLSR and can be used to accurately predict organic matter contents. Thus, our findings offer brand new methods for predicting SOM.
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.
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.
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