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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Data availability
The datasets generated during and/or analyzed during the current study are available in the European soil data center (ESDAC) repository, (https://esdac.jrc.ec.europa.eu/resource-type/datasets).
Abbreviations
- X:
-
Current batch
- Μ :
-
Mean
- Σ :
-
Standard deviation
- γ, β, ∈ :
-
Constant parameters
- j :
-
Scale parameter
- k :
-
Shift parameter
- ψ*:
-
Complex conjugate of mother wavelet
- Oi:
-
Measured value
- n :
-
Number of samples
- Pi:
-
Predicted value
- SD:
-
Standard deviation
- R 2 :
-
Coefficient of determination
- RMSE:
-
Root-mean-square error
- MAE:
-
Mean absolute error
- RPD:
-
Ratio of percent deviation
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Acknowledgements
The authors thank the Department of Agricultural Machinery Engineering, University of Tehran, for providing the workspace and other necessary resources to carry out the research. The LUCAS 2015 topsoil dataset used in this work was made available by the European Commission through the European Soil Data Centre managed by the Joint Research Centre (JRC) (http://esdac.jrc.ec.europa.eu/).
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MH-Z: methodology, software, conceptualization, writing—original draft preparation. MO: software, writing—original draft preparation, supervision, validation, project administration. FS: writing—original draft preparation, supervision, validation. HG-M: writing—reviewing and editing, validation.
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Hosseinpour-Zarnaq, M., Omid, M., Sarmadian, F. et al. A CNN model for predicting soil properties using VIS–NIR spectral data. Environ Earth Sci 82, 382 (2023). https://doi.org/10.1007/s12665-023-11073-0
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DOI: https://doi.org/10.1007/s12665-023-11073-0