Monitoring the Heavy Element of Cr in Agricultural Soils Using a Mobile Laser-Induced Breakdown Spectroscopy System with Support Vector Machine

Due to its complicated matrix effects, rapid quantitative analysis of chromium in agricultural soils is difficult without the concentration gradient samples by laser-induced breakdown spectroscopy. To improve the analysis speed and accuracy, two calibration models are built with the support vector machine method： one considering the whole spectra and the other based on the segmental spectra input. Considering the results of the multiple linear regression analysis, three segmental spectra are chosen as the input variables of the support vector regression （SVR） model. Compared with the results of the SVR model with the whole spectra input, the relative standard error of prediction is reduced from 3.18% to 2.61% and the running time is saved due to the decrease in the number of input variables, showing the robustness in rapid soil analysis without the concentration gradient samples.

Rapid Detection of Oil Pollution in Soil by Using Laser-Induced Breakdown Spectroscopy

Detection of oil pollution in soil has been carried out using laser-induced breakdown spectroscopy（LIBS）. A pulsed neodymium-doped yttrium aluminum garnet（Nd：YAG） laser（1,064 nm, 8 ns, 200 mJ） was focused onto pelletized soil samples. Emission spectra were obtained from oil-contaminated soil and clean soil. The contaminated soil had almost the same spectrum profile as the clean soil and contained the same major and minor elements. However, a C–H molecular band was clearly detected in the oil-contaminated soil, while no C–H band was detected in the clean soil. Linear calibration curve of the C–H molecular band was successfully made by using a soil sample containing various concentrations of oil. The limit of detection of the C–H band in the soil sample was 0.001 mL/g. Furthermore, the emission spectrum of the contaminated soil clearly displayed titanium（Ti） lines, which were not detected in the clean soil. The existence of the C–H band and Ti lines in oil-contaminated soil can be used to clearly distinguish contaminated soil from clean soil. For comparison, the emission spectra of contaminated and clean soil were also obtained using scanning electron microscope-energy dispersive X-ray（SEM/EDX） spectroscopy,showing that the spectra obtained using LIBS are much better than using SEM/EDX, as indicated by the signal to noise ratio（S/N ratio）.

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).

The use of Vis-NIR-SWIR spectroscopy in the prediction of soil available ions after application of rock powder

Some of the problems attributed to traditional laboratory analyses that limit the correct assessment of the nutrient contents in the soil are time requirements and high cost of the soil nutrient determinations.To solve these problems,a study was carried out to evaluate the use of visible,near-infrared,and short-wave infrared(Vis-NIR-SWIR)spectroscopy in the prediction of soil available ions submitted to the application of rock powders.The study was carried out on an Arenosol in ParanavaíCity/Brazil.Treatments(rock powders)were arranged within a split-plot system designed in randomized blocks with four repetitions.Sugarcane was cultivated for 14 months after the application of rock powders.Later,96 soil samples were collected for measuring the pH and available ions P,K^(+),Ca^(2+),Mg^(2+),S-SO_(4)^(2-),Si,Cu^(2+),Fe^(2+),Mn^(2+),and Zn^(2+)as well as spectral reading through a Vis-NIR-SWIR spectroradiometer to predict the soil chemical attributes through the partial least square regression(PLS)technique.The results showed that the elements K^(+),Ca^(2+),Mg^(2+),Cu^(2+),and Fe^(2+)could be predicted with a reasonable rightness degree(R^(2)_(p)>0.50,RPDp>1.40)from spectral models.However,for the attributes pH,P,S-SO_(4)^(2-),Si,Mn^(2+),and Zn^(2+),there were no satisfactory models(R^(2)_(p)<0.50,RPDp<1.40).Thus,the application of rock powder changed the spectral curves and,because of that,allows the building of PLS models to predict the elements K^(+),Ca^(2+),Mg^(2+),Cu^(2+),and Fe^(2+).Therefore,Vis-NIR-SWIR spectroscopy is a promising alternative to the routine analyses of soil fertility since it has advantages such as fast analytical speed,low cost,easy to operate,non-destructive,and environmentally friendly,because it does not use harmful chemicals.

Soil texture prediction through stratification of a regional soil spectral library

Knowing the spatial distribution of soil texture,which is a physical property,is essential to support agricultural and environmental decision making.Soil texture can be estimated using visible,near infrared,and shortwave infrared(Vis-NIR-SWIR)spectroscopy.However,the performance of spectroscopic models is variable because of soil heterogeneity.Currently,few studies address the effects of soil sample variability on the performance of the models,especially for larger spectral libraries that include soils that are more heterogeneous.Therefore,the objectives of this study were to:i)apply Vis-based color parameters on the stratification of a regional soil spectral library;ii)evaluate the performance of the predictive models generated from the spectral library stratification;iii)compare the performance of stratified models(SMs)and the model without stratification(WSM),and iv)explain possible changes in prediction accuracy based on the SMs.Thus,a regional soil spectral library with 1535 samples from the State of Santa Catarina,Brazil was used.Soil reflectance data were obtained by Vis-NIR-SWIR spectroscopy in the laboratory using a spectroradiometer covering the 350–2500 nm spectral range.Sand,silt,and clay fractions were determined using the pipette method.Twenty-two components of color parameters were derived from the Vis spectrum using the colorimetric models.A cubist regression algorithm was used to assess the accuracy of the applicability of the initial models(SMs and WSM)and of the validation between the clusters.Fractional order derivatives(FODs)at 0.5,1.5,and 2 intervals were used to explain possible changes in the performance of the SMs.The SMs with higher contents of clay and iron oxides obtained the highest accuracy,and the most important spectral bands were identified,mainly in the 480–550 and 850–900 nm ranges and the 1400,1900,and 2200 nm bands.Therefore,stratification of soil spectral libraries is a good strategy to improve regional assessments of soil resources,reducing prediction errors in the qualitative determination of soil properties.