AlradSpectra: a Quantification Tool for Soil Properties Using Spectroscopic Data in R

Dotto, André Carnieletto; Dalmolin, Ricardo Simão Diniz; Caten, Alexandre ten; Gris, Diego José; Ruiz, Luis Fernando Chimelo

doi:10.1590/18069657rbcs20180263

Cited by 7 publications

(5 citation statements)

References 19 publications

(18 reference statements)

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“…For NIRS readings, the soil samples (oven-dried, grounded, and sieved to 2.0 mm openings) were placed in the machine cell and subjected to analysis in duplicate, with readings at every 2 nm and wavelengths range from 1100 to 2500 nm. Thereafter, spectra were imported and analyzed using R (R CORE TEAM, 2019) program and the graphical interface AlradSpectra (DOTTO et. al., 2019).…”

Section: Methodsmentioning

confidence: 99%

Prediction of soil organic matter and clay contents by near-infrared spectroscopy - NIRS

et al. 2020

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Among the soil constituents, special attention is given to soil organic matter (SOM) and clay contents, since, among other aspects, they are key factors to nutrient retention and soil aggregates formation, which directly affect the crop production potential. The methods commonly used for the quantification of these constituents have some disadvantages, such as the use of chemical reactants and waste generation. An alternative to these methods is the near-infrared spectroscopy (NIRS) technique. The aim of this research is to evaluate models for SOM and clay quantification in soil samples using spectral data by NIRS. A set (n = 400) of soil samples previously analyzed by traditional methods were used to generate a NIRS calibration curve. The clay content was determined by the hydrometer method while SOM content was determined by sulfochromic solution. For calibration, we used the original spectra (absorbance) and spectral pretreatment (Savitzky-Golay smoothing derivative) in the following models: multiple linear regression (MLR), partial last squares regression (PLSR), support vector machine (SVM) and Gaussian process regression (GPR). The curve validation was performed with the SVM model (best performance in the calibration based on R² and RMSE) in two ways: with 40 random samples from the calibration set and another set with 200 new unknown samples. The soil clay content affects the predictive ability of the calibration curve to estimate SOM content by NIRS. Validation curves showed poorer performance (lower R² and higher RMSE) when generated from unknown samples, where the model tends to overestimate the lower levels and to underestimate the higher levels of clay and SOM. Despite the potential of NIRS technique to predict these attributes, further calibration studies are still needed to use this technique in soil analysis laboratories.

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Section: Methodsmentioning

confidence: 99%

Prediction of soil organic matter and clay contents by near-infrared spectroscopy - NIRS

et al. 2020

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“…These shortcomings were removed when the images were made available to general users in pre-processed and georeferenced data format. Nowadays, we have a similar situation, where many SSLs and software processing are available [41] but did not make the bridge to making their use easier for the end-users, and thus they (farmers, consulters, others) cannot see the importance. As the first step on a learning curve, why not start delivering the spectral soil products directly to users?…”

Section: Introduction and Contextualizationmentioning

confidence: 99%

The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication

et al. 2022

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Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible–Near–Short-wave infrared (vis–NIR–SWIR) and Mid-infrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis–NIR–SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique.

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“…We used SG with the first derivative with a first-order polynomial and window size of 5-nm (Figure 2d). The soil spectra preprocessing was performed using the Alradspectra graphical user interface in R programming (Dotto et al, 2019). The setting parameters for SNV and SG were selected after we performed the initial tests.…”

Section: Soil Spectral Acquisition and Preprocessingmentioning

confidence: 99%

Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil

Mendes¹,

Boechat²,

Gualberto³

et al. 2021

Revista Brasileira De Ciência Do Solo

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Soil chemical and physical analyses are the major sources of data for agriculture. However, traditional soil analyses are time-consuming, not cost-efficient, and not environmentally friendly. An alternative to traditional soil analyses is soil spectroscopy. This technique is a low-cost and quick analytical method, which can be implemented in a laboratory and/or in-situ. Nevertheless, some spectrometers are expensive and do not contemplate the entire spectrum. Despite this limitation, the main objective of the study was to create a soil spectral library of the Piauí State using only the 1000-2500 nm range. In this sense, it was evaluated and standardized the soil spectral library by accessing the combination of smoothing, standard normal variate, continuum removal, and Savitzky-Golay derivative spectral preprocessing procedures with partial least squares, random forest, and cubist machine learning algorithms. It was collected 262 geo-referenced soil samples at the layer of 0.00-0.20 m across the entire Piauí State, representing most of its soil variability. The soil properties evaluated were pH(H 2 O), sand, clay, and soil organic carbon (SOC) contents. This study demonstrated that the Standard Normal Variate was one of the most promising preprocessing procedures to improve model predictions for pH(H 2 O), sand, and clay. For SOC and pH, the best overall results were without preprocessing the soil spectra. Moreover, the cubist model was the most accurate in predicting soil properties. Finally, our study showed evidence of the potential and feasibility of using this soil spectral library to estimate soil properties such as pH(H 2 O), sand, clay, and SOC.

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AlradSpectra: a Quantification Tool for Soil Properties Using Spectroscopic Data in R

Cited by 7 publications

References 19 publications

Prediction of soil organic matter and clay contents by near-infrared spectroscopy - NIRS

Prediction of soil organic matter and clay contents by near-infrared spectroscopy - NIRS

The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication

Soil spectral library of Piauí State using machine learning for laboratory analysis in Northeastern Brazil

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