Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Li, Dan; Chen, Xiuzhi; Peng, Zhe; Chen, Shuisen; Chen, Weiqi; Han, Liusheng; Li, Yongjie

doi:10.1016/j.still.2012.03.013

Cited by 39 publications

(16 citation statements)

References 42 publications

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“…However, incomplete ignition, presence of non-ignitable SOM or ignitable soil inorganic matter may contribute to wrong estimation. Spectroscopic characteristics measured using Vis-NIR or NIR sensors have shown promise in predicting SOM in laboratory ex situ condition (C ecillon et al, 2009;Li et al 2012) or at field in situ condition (Kodaira & Shibusawa, 2013;Wenjun, Zhou, Jingyi, & Shuo, 2014). Shepherd and Walsh (2002) and Viscarra Rossel, Cattle, Ortega, and Fouad (2009) reported a strong correlation between SOM and laboratory measured MIR.…”

Section: Introductionmentioning

confidence: 99%

Microscope-based computer vision to characterize soil texture and soil organic matter

Sudarsan

Biswas

et al. 2016

Biosystems Engineering

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Section: Introductionmentioning

confidence: 99%

Microscope-based computer vision to characterize soil texture and soil organic matter

Sudarsan

Biswas

et al. 2016

Biosystems Engineering

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“…The PLS regression is a bilinear regression method that extracts a small number of factors, which are a combination of the independent variables, and uses these factors as a regression generator for the dependent variables or chemicallymeasured values (Maleki et al, 2006). Although PLS regression has many advantages, such as its simplicity, robustness, predictability, precision, and clearly quantitative explanations, the main disadvantage is that PLS regression does not provide a quantitative explanation for the relationship between predictor variables and response variables, and it does not support re-use of model algorithms between different instrumentations (Li et al, 2012). …”

Section: Optical Measurementsmentioning

confidence: 99%

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Tekin

Tümsavaş

Mouazen

2014

Rev. Bras. Ciênc. Solo

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SUMMARYVisible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.Index terms: modeling, prediction, vis-NIR.( RESUMO: COMPARAÇÃO ENTRE REDE NEURAL ARTIFICIAL E REGRESSÃO POR MÍNIMOS QUADRADOS PARCIAIS NA PREDIÇÃO DO CARBONO ORGÂNICO DO SOLO E DO pH, EM DIFERENTES NÍVEIS DE UMIDADE DO SOLO UTILIZANDO ESPECTROSCOPIA NO VIS-IVPA espectroscopia de infravermelho-visível próximo (vis-IVP) é amplamente usada para detectar propriedades do solo. O objetivo deste estudo foi avaliar o efeito combinado da umidade do solo e do algoritmo de modelagem na predição do carbono orgânico do solo (COS) e pH. Para tanto, foram utilizadas 270 amostras de solo para comparar a eficiência da predição da regressão por mínimos quadrados parciais (PLS) e da rede neural artificial (RNA) na modelagem do COS e pH, em diferentes níveis de umidade. Antes da determinação espectral, as amostras de solo secas foram pesadas para discriminar a quantidade de água a ser adicionada para que essa atingisse os níveis de umidade gravimétrica de 5, 10, 15, 20 e 25 %. Para obter a resposta espectral das amostras de solo, no modo de reflectância difusa, foi utilizado um espectrofotômetro de fibra ótica, na faixa do infravermelho-visível próximo (350-2500 nm). O pré-processamento dos dados espectrais e a análise pela regress do PLS foram implementados no software Unscrambler®; enquanto na modelagem pela RNA, foi utilizado o software Statistica® (Version 11, StatSoft Inc. USA). Para o COS, o melhor resultado da predição foi obtido utilizando a RNA (RMSEP = 0....

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“…Nowadays, sampling‐free optical spectroscopy methods of soil analysis operating in the visible (Vis), near‐infrared (NIR), and mid‐infrared (MIR) regions are increasingly investigated and used . A few researchers have addressed the problem of spectroscopic determination of total petroleum hydrocarbons (TPH) in the soil and proved the feasibility of the quantitative analysis of hydrocarbons in oil‐contaminated soils by diffuse reflection NIR spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of total hydrocarbons and water in oil‐contaminated soils with attenuated total reflection infrared spectroscopy

Guryanova

Ermakov

Galyanin

et al. 2016

Journal of Chemometrics

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The feasibility of direct (ie, without sample preparation) quantitative analysis of total hydrocarbons and water in oil-contaminated soils using mid-infrared spectroscopy and an attenuated total reflection (ATR) probe has been investigated. Spectral characteristics of unpolluted and oil-contaminated soils composed of sand, clay, dolomite, and humus have been studied over the full mid-infrared range (4000-400 cm). Spectra of 25 typical soil samples containing varying levels of oil and water have been analyzed using a chalcogenide infrared fiber-based probe with a ZrO 2 crystal as an ATR element. The spectral data were used to build calibration models for the analysis of hydrocarbon contamination as well as moisture content of soil samples. The low quality of ATR spectra of drier samples and variable spectral intensity inherent in the ATR measurement of solids has been overcome by suitable data processing. Further improvement of the model performance has been achieved using a variable selection based on the modified genetic algorithm. Our proposed method allows the determination of oil and moisture content in soils with accuracies of 1.1% and 0.6%, respectively, which is sufficient for a number of practical applications. The reported results may be used to develop portable devices for measuring petroleum and water content of soils.

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Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

Cited by 39 publications

References 42 publications

Microscope-based computer vision to characterize soil texture and soil organic matter

Microscope-based computer vision to characterize soil texture and soil organic matter

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Quantitative analysis of total hydrocarbons and water in oil‐contaminated soils with attenuated total reflection infrared spectroscopy

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