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Articles | Volume 6, issue 2
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.
https://doi.org/10.5194/soil-6-565-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 6, issue 2
Original research article
 | 
17 Nov 2020
Original research article |  | 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

Wartini Ng, Budiman Minasny, Wanderson de Sousa Mendes, and José Alexandre Melo Demattê

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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.
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