Developing a model for soil potassium estimation using spectrometry data

Mobasheri, Mohammad Reza; Amani, Meisam; Fathi-Almas, Roghayeh; Mahdavi, Sahel; Zabihi, Hamid Reza

doi:10.1080/00103624.2020.1733002

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…The obtained results in this work suggested that good predictions of TN, SOC, S, P, and pH were obtained using the portable NIR spectrometer. [76] developed a new MLR model for the proper estimation of soil potassium content. The calibrated model showed a high potential for soil potassium prediction.…”

Section: Prediction Of Multiple Soil Propertiesmentioning

confidence: 99%

Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances–A review

Barra

Haefele

Sakrabani

et al. 2021

TrAC Trends in Analytical Chemistry

101

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Section: Prediction Of Multiple Soil Propertiesmentioning

confidence: 99%

Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances–A review

Barra

Haefele

Sakrabani

et al. 2021

TrAC Trends in Analytical Chemistry

101

View full text Add to dashboard Cite

“…After comparing partial least squares (PLS) with three different feature wavelength extraction methods, Ning [12] established a classification model of soil organic matter and total nitrogen content using a successive projections algorithm-extreme learning machine model. Mobasheri [13] used spectral data to model potassium in three different types of soils with an R 2 of 0.95-0.98. Zhang [14] developed partial least squares regression (PLSR) and support vector machine (SVM) models for soil total nitrogen detection using near-infrared spectroscopy, and obtained better results.…”

Section: Introductionmentioning

confidence: 99%

NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States

He,

Zhang,

et al. 2023

Sensors

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Soil fertility is vital for the growth of tea plants. The physicochemical properties of soil play a key role in the evaluation of soil fertility. Thus, realizing the rapid and accurate detection of soil physicochemical properties is of great significance for promoting the development of precision agriculture in tea plantations. In recent years, spectral data have become an important tool for the non-destructive testing of soil physicochemical properties. In this study, a support vector regression (SVR) model was constructed to model the hydrolyzed nitrogen, available potassium, and effective phosphorus in tea plantation soils of different grain sizes. Then, the successful projections algorithm (SPA) and least-angle regression (LAR) and bootstrapping soft shrinkage (BOSS) variable importance screening methods were used to optimize the variables in the soil physicochemical properties. The findings demonstrated that soil particle sizes of 0.25–0.5 mm produced the best predictions for all three physicochemical properties. After further using the dimensionality reduction approach, the LAR algorithm (R2C = 0.979, R2P = 0.976, RPD = 6.613) performed optimally in the prediction model for hydrolytic nitrogen at a soil particle size of 0.25~0.5. The models using data dimensionality reduction and those that used the BOSS method to estimate available potassium (R2C = 0.977, R2P = 0.981, RPD = 7.222) and effective phosphorus (R2C = 0.969, R2P = 0.964, RPD = 5.163) had the best accuracy. In order to offer a reference for the accurate detection of soil physicochemical properties in tea plantations, this study investigated the modeling effect of each physicochemical property under various soil particle sizes and integrated the regression model with various downscaling strategies.

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NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States

He,

Zhang,

et al. 2023

Preprint

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Developing a model for soil potassium estimation using spectrometry data

Cited by 3 publications

References 22 publications

Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances–A review

Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances–A review

NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States

NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States

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