Machine learning methods for soil moisture prediction in vineyards using digital images

Chantal Saad Hajjar; Celine Hajjar; Michel Esta; Yolla Ghorra Chamoun

doi:10.1051/e3sconf/202016702004

All issues

Volume 167 (2020)

E3S Web Conf., 167 (2020) 02004

References

Open Access

Issue		E3S Web Conf. Volume 167, 2020 2020 11^th 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

S. Priori, S. Pellegrini, R. Perria, S. Puccioni, P. Storchi, G. Valboa and E. A. C. Costantini, Scale effect of terroir under three contrasting vintages in the Chianti Classico area (Tuscany, Italy), Geoderma, 334, 99-112 (2019). [Google Scholar]
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C. V. Leeuwen, 9 Terroir: the effect of the physical environment on vine growth, grape ripening and wine sensory attributes, in Managing Wine Quality, A. G. Reynolds, Ed., Woodhead Publishing, 273-315 (2010). [CrossRef] [Google Scholar]
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P. Dobriyal, A. Qureshi, R. Badola and S. A. Hussain, A review of the methods available for estimating soil moisture and its implications for water resource management, J. Hydrol 458-459, 110-117 (2012). [CrossRef] [Google Scholar]
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M. K. Gill, T. Asefa, M. W. Kemblowski and M. McKee, Soil moisture prediction using support vector machines, J AM WATER RESOUR AS, 42, 1033-1046 (2006). [CrossRef] [Google Scholar]
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L. Pasolli, C. Notarnicola and L. Bruzzone, Estimating Soil Moisture With the Support Vector Regression Technique, IEEE GEOSCI REMOTE S, 8, 1080-1084, 11 (2011). [CrossRef] [Google Scholar]
S. S. Haykin, Neural networks and learning machines, Third ed., Pearson Education (2009). [Google Scholar]
I. N. Silva, D. Hernane Spatti, R. Andrade Flauzino, L. H. B. Liboni and S. F. Reis Alves, Multilayer Perceptron Networks, in Artificial Neural Networks: A Practical Course, Cham, Springer International Publishing, 55-115 (2017). [Google Scholar]
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M. Aitkenhead, C. Cameron, G. Gaskin, B. Choisy, M. Coull and H. Black, Digital RGB photography and visible-range spectroscopy for soil composition analysis, Geoderma, 313, 265-275 (2018). [Google Scholar]
Y. N. Vodyanitskii and A. T. Savichev, The influence of organic matter on soil colour using the regression equations of optical parameters in the system CIE-L*a*b*, Ann. Agrar. Sci., 15, 380-385 (2017). [CrossRef] [Google Scholar]

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[1] S. Priori, S. Pellegrini, R. Perria, S. Puccioni, P. Storchi, G. Valboa and E. A. C. Costantini, Scale effect of terroir under three contrasting vintages in the Chianti Classico area (Tuscany, Italy), Geoderma, 334, 99-112 (2019). [Google Scholar]

[2] E. A. C. Costantini, R. Lorenzetti and G. Malorgio, A multivariate approach for the study of environmental drivers of wine economic structure, Land use policy, 57, 53-63 (2016). [Google Scholar]

[3] P. Clingeleffer, Terroir: The Application of an Old Concept in Modern Viticulture, 277-288 (2014). [Google Scholar]

[4] C. V. Leeuwen, 9 Terroir: the effect of the physical environment on vine growth, grape ripening and wine sensory attributes, in Managing Wine Quality, A. G. Reynolds, Ed., Woodhead Publishing, 273-315 (2010). [CrossRef] [Google Scholar]

[5] S. U. Susha Lekshmi, D. N. Singh and M. S. Baghini, A critical review of soil moisture measurement, Measurement, 54, 92-105 (2014). [CrossRef] [Google Scholar]

[6] P. Dobriyal, A. Qureshi, R. Badola and S. A. Hussain, A review of the methods available for estimating soil moisture and its implications for water resource management, J. Hydrol 458-459, 110-117 (2012). [CrossRef] [Google Scholar]

[7] C. T. Altendorf, R. Elliott, E. W. Stevens and M. L. Stone, Development and validation of a neural network model for soil water content prediction with comparison to regression techniques, T ASABE, 42, 691-700 (1999). [CrossRef] [Google Scholar]

[8] M. K. Gill, T. Asefa, M. W. Kemblowski and M. McKee, Soil moisture prediction using support vector machines, J AM WATER RESOUR AS, 42, 1033-1046 (2006). [CrossRef] [Google Scholar]

[9] A. Elshorbagy and K. Parasuraman, On the relevance of using artificial neural networks for estimating soil moisture content, J. Hydrol., 362, 1-18 (2008). [CrossRef] [Google Scholar]

[10] M. Persson, Estimating Surface Soil Moisture from Soil Colour Using Image Analysis, VADOSE ZONE J. VADOSE ZONE J, 4, 1119-1122, 11 (2005). [CrossRef] [Google Scholar]

[11] J. F. C. d. Santos, H. R. F. Silva, F. A. C. Pinto and I. R. d. Assis, Use of digital images to estimate soil moisture, Rev. bras. eng. agríc. ambient., 20, 1051-1056, 12 (2016). [CrossRef] [Google Scholar]

[12] L. Pasolli, C. Notarnicola and L. Bruzzone, Estimating Soil Moisture With the Support Vector Regression Technique, IEEE GEOSCI REMOTE S, 8, 1080-1084, 11 (2011). [CrossRef] [Google Scholar]

[13] S. S. Haykin, Neural networks and learning machines, Third ed., Pearson Education (2009). [Google Scholar]

[14] I. N. Silva, D. Hernane Spatti, R. Andrade Flauzino, L. H. B. Liboni and S. F. Reis Alves, Multilayer Perceptron Networks, in Artificial Neural Networks: A Practical Course, Cham, Springer International Publishing, 55-115 (2017). [Google Scholar]

[15] M. T. Hagan and M. B. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE T NEUR NET LEAR, 5, 989-993, 11 (1994). [CrossRef] [PubMed] [Google Scholar]

[16] V. N. Vapnik, Methods of Function Estimation, in The Nature of Statistical Learning Theory, New, York: Springer New York, 181-224 (2000). [CrossRef] [Google Scholar]

[17] Deep Learning Toolbox, MATLAB, R2018b. [Google Scholar]

[18] Statistics and Machine Learning Toolbox, MATLAB, R2018b. [Google Scholar]

[19] M. Aitkenhead, C. Cameron, G. Gaskin, B. Choisy, M. Coull and H. Black, Digital RGB photography and visible-range spectroscopy for soil composition analysis, Geoderma, 313, 265-275 (2018). [Google Scholar]

[20] Y. N. Vodyanitskii and A. T. Savichev, The influence of organic matter on soil colour using the regression equations of optical parameters in the system CIE-L*a*b*, Ann. Agrar. Sci., 15, 380-385 (2017). [CrossRef] [Google Scholar]