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01.08.2023 | Original Article

A CNN model for predicting soil properties using VIS–NIR spectral data

verfasst von: Mohammad Hosseinpour-Zarnaq, Mahmoud Omid, Fereydoon Sarmadian, Hassan Ghasemi-Mobtaker

Erschienen in: Environmental Earth Sciences | Ausgabe 16/2023

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Abstract

This research aims to develop a novel deep learning-based model for predicting soil properties based on visible and near-infrared (VIS–NIR) spectroscopic data. Soil samples were collected from the European topsoil dataset prepared by the LUCAS project provides various soil physicochemical properties analyzed within 28 EU countries (including sand, silt, clay, pH, organic carbon, calcium carbonates (CaCO3), and N). In this study, one-dimensional (1D) convolutional neural network (CNN) models were developed using absorbance spectral data. The performance of feature learning from discrete wavelet transforms as a powerful preprocessing method was tested. Moreover, the results of the proposed CNN model were compared with partial least squares regression (PLSR) with raw absorbance and optimum classical preprocessing (Savitzky–Golay smoothing with first-order derivative). The ratio of percent deviation (RPD) of CNN with absorbance data for prediction of soil OC, CaCO3, pH, N, sand, silt, and clay content were 4.02, 3.89, 2.82, 3.02, 1.63, 1.43, and 2.16, respectively. While the RPD of PLSR with optimal preprocessing of absorbance data for predicting the mentioned parameters were 2.89, 3.00, 2.79, 2.50, 1.37, 1.27, and 1.84, respectively. The study demonstrated the feasibility of using deep learning-based models and VIS–NIR spectral data as a rapid non-destructive tool for the assessment of important soil properties.

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Literatur
Zurück zum Zitat Maia AJ, da Silva YJ, do Nascimento CW, Veras G, Escobar ME, Cunha CS, da Silva YJ, Nascimento RC, de Souza Pereira LH (2020) Near-infrared spectroscopy for the prediction of rare earth elements in soils from the largest uranium-phosphate deposit in Brazil using PLS, iPLS, and iSPA-PLS models. Environ Monit Assess 192:1–14. https://doi.org/10.1007/s10661-020-08642-2CrossRef Maia AJ, da Silva YJ, do Nascimento CW, Veras G, Escobar ME, Cunha CS, da Silva YJ, Nascimento RC, de Souza Pereira LH (2020) Near-infrared spectroscopy for the prediction of rare earth elements in soils from the largest uranium-phosphate deposit in Brazil using PLS, iPLS, and iSPA-PLS models. Environ Monit Assess 192:1–14. https://​doi.​org/​10.​1007/​s10661-020-08642-2CrossRef
Metadaten
Titel
A CNN model for predicting soil properties using VIS–NIR spectral data
verfasst von
Mohammad Hosseinpour-Zarnaq
Mahmoud Omid
Fereydoon Sarmadian
Hassan Ghasemi-Mobtaker
Publikationsdatum
01.08.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
Environmental Earth Sciences / Ausgabe 16/2023
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI
https://doi.org/10.1007/s12665-023-11073-0