Articles | Volume 6, issue 2
SOIL, 6, 565–578, 2020
https://doi.org/10.5194/soil-6-565-2020
SOIL, 6, 565–578, 2020
https://doi.org/10.5194/soil-6-565-2020

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 et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (14 Apr 2020) by Bas van Wesemael
AR by Wartini Ng on behalf of the Authors (12 May 2020)  Author's response    Manuscript
ED: Reconsider after major revisions (20 May 2020) by Bas van Wesemael
ED: Referee Nomination & Report Request started (02 Jun 2020) by Bas van Wesemael
RR by Anonymous Referee #2 (02 Jul 2020)
RR by Anonymous Referee #3 (27 Aug 2020)
ED: Publish subject to minor revisions (review by editor) (27 Aug 2020) by Bas van Wesemael
AR by Wartini Ng on behalf of the Authors (28 Aug 2020)  Author's response    Manuscript
ED: Publish subject to technical corrections (14 Sep 2020) by Bas van Wesemael
ED: Publish subject to technical corrections (30 Sep 2020) by Kristof Van Oost(Executive Editor)
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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.