Issue |
E3S Web Conf.
Volume 167, 2020
2020 11th International Conference on Environmental Science and Development (ICESD 2020)
|
|
---|---|---|
Article Number | 02004 | |
Number of page(s) | 7 | |
Section | Water Resources | |
DOI | https://doi.org/10.1051/e3sconf/202016702004 | |
Published online | 24 April 2020 |
Machine learning methods for soil moisture prediction in vineyards using digital images
1
Ecole Supérieure d'Ingénieurs d'Agronomie Méditerranéenne Université Saint-Joseph de Beyrouth, Beirut, Lebanon
2
Ecole Supérieure d'Ingénieurs de Beyrouth Université Saint-Joseph de Beyrouth, Beirut, Lebanon
3
Institut de Gestion des Entreprises Université Saint-Joseph de Beyrouth, Beirut, Lebanon
* Corresponding author: chantal.hajjar@usj.edu.lb
In this paper, we propose to estimate the moisture of vineyard soils from digital photography using machine learning methods. Two nonlinear regression models are implemented: a multilayer perceptron (MLP) and a support vector regression (SVR). Pixels coded with RGB colour model extracted from soil digital images along with the associated known soil moisture levels are used to train both models in order to predict moisture content from newly acquired images. The study is conducted on samples of six soil types collected from Chateau Kefraya terroirs in Lebanon. Both methods succeeded in forecasting moisture giving high correlation values between the measured moisture and the predicted moisture when tested on unknown data. However, the method based on SVR outperformed the one based on MLP yielding Pearson correlation coefficient values ranging from 0.89 to 0.99. Moreover, it is a simple and noninvasive method that can be adopted easily to detect vineyards soil moisture.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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