Articles | Volume 6, issue 2
https://doi.org/10.5194/soil-6-565-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-6-565-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
17 Nov 2020
Original research article |
| 17 Nov 2020
| 17 Nov 2020
The influence of training sample size on the accuracy of deep learning models for the prediction of soil properties with near-infrared spectroscopy data
School of Life and Environmental Sciences and Sydney Institute of
Agriculture, The University of Sydney, NSW, Australia
Budiman Minasny
School of Life and Environmental Sciences and Sydney Institute of
Agriculture, The University of Sydney, NSW, Australia
Wanderson de Sousa Mendes
Department of Soil Science, Luiz de Queiroz College of
Agriculture, University of São Paulo, Piracicaba, São Paulo, 13418-900, Brazil
José Alexandre Melo Demattê
Department of Soil Science, Luiz de Queiroz College of
Agriculture, University of São Paulo, Piracicaba, São Paulo, 13418-900, Brazil
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Short summary
The number of samples utilised to create predictive models affected model performance. This research compares the number of samples needed by a deep learning model to outperform the traditional machine learning models using visible near-infrared spectroscopy data for soil properties predictions. The deep learning model was found to outperform machine learning models when the sample size was above 2000.
The number of samples utilised to create predictive models affected model performance. This...
