Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging,Laboratory VNIR Spectroscopy and Their Combination

Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)layer.The data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset combination.According to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),respectively.The data fusion has improved the soil property prediction.The highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS).

In-situ soil texture classification and physical clay content measurement based on multi-source information fusion

Soil texture is one of the most important soil characteristics that affect soil properties.Rapid acquisition of soil texture information is of great significance for accurate farmland management.Traditional soil texture analysis methods are relatively complicated and cannot meet the requirements of temporal and spatial resolution.This research introduced a self-developed vehicle-mounted in-situ soil texture detection system,which can predict the type of soil texture and the particle composition of the texture,and obtain real-time data during the measurement process without preprocessing the soil samples.The detection system is mainly composed of a conductivity measuring device,a camera,an auxiliary mechanical structure,and a control system.The soil electrical conductivity(ECa)and the texture features extracted from the surface image were input into the embedded model to realize real-time texture analysis.In order to find the best model suitable for the detection system,measurements were carried out in three test fields in Northeast and North China to compare the performance of different models applied to the detection system.The results showed that for soil texture classification,ExtraTrees performed best,with Precision,Recall,and F1 all being 0.82.For particle content of soil texture prediction,the R2 of ExtraTrees was 0.77,and RMSE and MAPE were 74.72 and 39.58.It was observed that ECa,Moment of inertia,and Entropy had larger weights in the drawn model influence weight map,and they are the main contributors to predicting soil texture.These results showed the potential of the vehicle-mounted in-situ soil texture detection system,which can provide a basis for fast,cost-effective,and efficient soil texture analysis.