VisNIR integrated multi-sensing penetrometer for in situ high-resolution vertical soil sensing

Wijewardane, Nuwan K.; Hetrick, Sarah; Ackerson, Jason P.; Morgan, Cristine L.S.; Ge, Yufeng

doi:10.1016/j.still.2020.104604

Cited by 32 publications

(12 citation statements)

References 53 publications

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“…Instead, the on-line collected fresh spectra having a wide range of SMC can be used, after the correction of SMC effect to estimate SOC [60]. Both Ackerson et al [16] and Wijewardane et al [62] demonstrated that the utilization of EPO-based in situ spectra is essential for generating the initial EPO. Our results of EPO correction proposed that the projection matrix based-on the on-line spectra and corresponding air-dry spectra, when applied to the on-line spectral library with a varied moisture content can decrease logistical necessities by efficiently removing the effect of SMC from the spectra and, therefore, improving the prediction accuracy of SOC.…”

Section: Performance Of the Cubist Models After Correction For Moisturementioning

confidence: 99%

Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect

2020

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It is well-documented in the visible and near-infrared reflectance spectroscopy (VNIRS) studies that soil moisture content (SMC) negatively affects the prediction accuracy of soil attributes. This work was undertaken to remove the negative effect of SMC on the on-line prediction of soil organic carbon (SOC). A mobile VNIR spectrophotometer with a spectral range of 305–1700 nm and spectral resolution of 1 nm (CompactSpec, Tec5 Technology, Germany) was used for the spectral measurements at four farms in Flanders, Belgium. A total of 381 fresh soil samples were collected and divided into a calibration set (264) and a validation set (117). The validation samples were processed (air-dried and grind) and scanned with the same spectrophotometer in the laboratory. Three SMC correction methods, namely, external parameter orthogonalization (EPO), piecewise direct standardization (PDS), and orthogonal signal correction (OSC) were used to correct the on-line fresh spectra based-on its corresponding laboratory spectra. Then, the Cubist machine learning method was used to develop calibration models of SOC using the on-line spectra (after correction) of the calibration set. Results indicated that the EPO-Cubist outperformed the PDS-Cubist and the OSC-Cubist, with considerable improvements in the prediction results of SOC (coefficient of determination (R2) = 0.76, ratio of performance to deviation (RPD) = 2.08, and root mean square error of prediction (RMSEP) = 0.12%), compared with the corresponding uncorrected on-line spectra (R2 = 0.55, RPD = 1.24, and RMSEP = 0.20%). It can be concluded that SOC can be accurately predicted on-line using the Cubist machine learning method, after removing the negative effect of SMC with the EPO method.

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Section: Performance Of the Cubist Models After Correction For Moisturementioning

confidence: 99%

Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect

2020

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“…These include spectra transformation, and dividing the spectral data into different classes of moisture content, each of which will have specific calibration models. Most reported spectra transformation methods are external parameter orthogonalization (EPO), orthogonal signal correction (OSC), piecewise direct standardization (PDS), and direct standardization (DS) (Wold et al, 1998;Pinto et al, 2012;Roudier et al, 2017;Liu et al, 2020;Nawar et al, 2020;Wijewardane et al, 2020). The EPO and OSC correct the vis-NIR spectra for orthogonality neutralization, while the DS and PDS algorithms transfer the sample spectra from one (e.g., on-line scanning) to another sampling mode (e.g., laboratory scanning).…”

Section: Introductionmentioning

confidence: 99%

Removal of external influences from on-line vis-NIR spectra for predicting soil organic carbon using machine learning

Munnaf

Mouazen

2022

CATENA

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External factors including moisture content negatively affect the prediction accuracy of soil organic carbon (SOC) using on-line visible and near-infrared (vis-NIR) spectroscopy. This study compared the performances of four algorithms to remove the moisture content effect [direct standardization (DS), piecewise direct standardization (PDS), external parameter orthogonalization (EPO), and orthogonal signal correction (OSC)] against noncorrected (NC) spectral models developed with partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and M5Rules regression. An on-line soil sensing platform coupled with a vis-NIR spectrophotometer (305-1700 nm) was used to scan twelve agricultural fields in Belgium and France. A total of 372 soil samples collected during the on-line measurement were divided into a calibration (260) and a prediction (112) dataset, using the Kennard-Stone algorithm. The latter set together with identical laboratorymeasured 112 dry soil spectra formed a transfer dataset to develop EPO, DS and PDS correction matrices. Results showed that models after EPO, PDS and OSC corrections resulted in improved accuracy [coefficient of determination (R 2 ) = 0.60-0.82, root mean square error (RMSE) = 16.1-5.7 g kg − 1 )], compared to the NC models (R 2 = 0.58-0.73, RMSE = 16.5-6.8 g kg − 1 ), whereas the DS (R 2 = − 0.10 to 0.26, RMSE = 26.8-21.9 g kg − 1 ) provided deteriorated prediction accuracy. The EPO and OSC models provided better prediction accuracy than that of the PDS corrected models. The OSC-M5Rules (R 2 = 0.82, RMSE = 5.7 g kg − 1 ) obtained the highest accuracy followed by EPO-M5Rules (R 2 = 0.74, RMSE = 6.7 g kg − 1 ) and NC-M5Rules (R 2 = 0.73, RMSE = 6.8 g kg − 1 ), which outperformed all PLSR, RF and SVM models. Therefore, on-line vis-NIR spectra should be corrected with the OSC algorithm before calibrating a machine learning model for accurate prediction of SOC.

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“…Alguns métodos podem ser utilizados para se compreender como variam em uma região da paisagem, para identificar a localização de alguns padrões contrastantes no solo, ou para refinar os limites das delimitações de unidade em mapas de solo (STOCK-MANN et al, 2015). Outros métodos podem ser usados para documentar as propriedades do solo em locais específicos que normalmente são utilizados como ferramenta para descrever perfis de solo e ajudar a caracterizar propriedades específicas (WIJEWARDANE et al, 2020). A mensuração pode ser obtida pela utilização do sensor in situ (no campo), normalmente com operadores móveis, ou a partir de amostras do solo que são coletadas e mensuradas ex situ (em laboratório), normalmente com operadores estáticos.…”

Section: Outras Técnicas De Sensoriamento Próximo Do Solounclassified

Avanços na observação e no conhecimento do solo via o sensoriamento próximo

Caten

Dalmolin

Silva

et al. 2021

RAC

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A agricultura emprega técnicas cada vez mais inovadoras na busca por otimizar insumos, maximizar a lucratividade e diminuir o impacto ambiental. Exemplo disso é o despontar da agricultura 4.0, na qual sensores coletem informações através do Sensoriamento Proximal do Solo. Esses métodos, chamados photon-based methods, empregam distintos comprimentos de onda da radiação eletromagnética para mensurar atributos e propriedades do solo in-situ ou ex-situ. Instituições nacionais de pesquisa têm produzido conhecimento relevante e contribuído para a formação de profissionais aptos a aplicar essas novas abordagens em ciência do solo. Nesse contexto, esta revisão bibliográfica teve como objetivo verter as principais técnicas de sensoriamento proximal em uma síntese acessível para estudantes, técnicos e pesquisadores.

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VisNIR integrated multi-sensing penetrometer for in situ high-resolution vertical soil sensing

Cited by 32 publications

References 53 publications

Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect

Machine Learning Based On-Line Prediction of Soil Organic Carbon after Removal of Soil Moisture Effect

Removal of external influences from on-line vis-NIR spectra for predicting soil organic carbon using machine learning

Avanços na observação e no conhecimento do solo via o sensoriamento próximo

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