Abstract
In agriculture, plant cultivation requires to take numerous decisions. One of the major problems is irrigation: an adequate irrigation decision must be made accordingly to the hydric status of the plant and soil, and the weather forecasts. In precision agronomy, this leads to the use of hydric sensors combined with a numerical growth plant model. Such models can not often be tuned by experts. We proposed an automatic parameter calibration of the potato growth model based on data collected in several open fields. As these parameter calibration problems are ill-posed, the associated black-box optimization problem is supposed to be multi-modal. We then compare the performances of two state-of-the-art Evolution Strategies which use different restart mechanisms to automatically tune the set of parameters on different crops and shows that multi-modal optimization methods may be recommended for such class of optimization problems.
Supported by WEENAT.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Some parameters can also have no meaning from a biological point of view.
- 2.
References
Tang, Y., Reed, P., Wagener, T.: How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration? Hydrol. Earth Syst. Sci. Discuss. 2(6), 2465–2520 (2005)
Gupta, H.V., Beven, K.J., Wagener, T.: Model calibration and uncertainty estimation. In: Encyclopedia of Hydrological Sciences (2006)
Whisler, F.D., et al.: Crop simulation models in agronomic systems. In: Advances in Agronomy, vol. 40, pp. 141–208. Elsevier (1986)
Raes, D., Steduto, P., Hsiao, T.C., Fereres, E.: Aquacrop–the FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron. J. 101(3), 438–447 (2009)
Ramat, E., Vandoorne, B.: Plant growth model for decision making support. Technical report, Université du Littoral Côte d’Opale, and ISA Lille (2002)
Beyer, H.-G.: The Theory of Evolution Strategies. Springer, New York (2001). https://doi.org/10.1007/978-3-662-04378-3
Rapin, J., Teytaud, O.: Nevergrad-a gradient-free optimization platform (2018). https://GitHub.com/FacebookResearch/Nevergrad
Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Number 15 in Problemata. Frommann-Holzboog (1973)
Li, X.: Multimodal optimization using niching methods, pp. 1–8. American Cancer Society (2016)
Preuss, M.: Multimodal Optimization by Means of Evolutionary Algorithms. NCS. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-07407-8_7
Ahrari, A., Deb, K., Preuss, M.: Multimodal optimization by covariance matrix self-adaptation evolution strategy with repelling subpopulations. Evol. Comput. 25(3), 439–471 (2017)
Auger, A., Hansen, N.: A restart CMA evolution strategy with increasing population size. In: 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1769–1776. IEEE (2005)
Kadioglu, S., Sellmann, M., Wagner, M.: Learning a reactive restart strategy to improve stochastic search. In: Battiti, R., Kvasov, D.E., Sergeyev, Y.D. (eds.) LION 2017. LNCS, vol. 10556, pp. 109–123. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69404-7_8
Teytaud, F., Teytaud, O.: Qr mutations improve many evolution strategies: a lot on highly multimodal problems. In: Proceedings of the 2016 GECCO Conference, pp. 35–36 (2016)
Schoenauer, M., Teytaud, F., Teytaud, O.: A rigorous runtime analysis for quasi-random restarts and decreasing stepsize. In: Hao, J.-K., Legrand, P., Collet, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds.) EA 2011. LNCS, vol. 7401, pp. 37–48. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35533-2_4
Beaujouan, V.: Modélisation des transferts d’eau et d’azote dans les sols et les Nappes. Développement d’un modèle conceptuel distribué. Application à de petits bassins versants. Ph.D., thesis, Ecole Nationale Supérieure Agronomique de Rennes (2001)
Allen, R.G., Pereira, L.S., Raes, D., Smith, M., et al.: Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage paper 56. FAO, Rome, vol. 300, no. 9 (1998). D05109
Teng, P.S., Johnson, K.B., Johnson, S.B.: Development of a simple potato growth model for use in crop-pest management. Agric. Syst. 19(3), 189–209 (1986)
Beaujouan, V., Durand, P., Ruiz, L.: Modelling the effect of the spatial distribution of agricultural practices on nitrogen fluxes in rural catchments. Ecol. Model. 137(1), 93–105 (2001)
Van Genuchten, M.T.: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils 1. Soil Sci. Soc. Am. J. 44(5), 892–898 (1980)
Hansen, N., Müller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol. Comput. 11(1), 1–18 (2003)
Hansen, N., Kern, S.: Evaluating the CMA evolution strategy on multimodal test functions. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 282–291. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30217-9_29
Acknowledgements
The authors would like to thank the WEENAT company in particular for the financing of the CIFRE thesis and for their material support. Experiments presented in this paper were carried out using the CALCULCO computing platform, supported by SCOSI/ULCO (Service COmmun du Système d’Information de l’Université du Littoral Côte d’Opale).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Dubois, A., Teytaud, F., Ramat, E., Verel, S. (2020). Automatic Calibration of a Farm Irrigation Model: A Multi-Modal Optimization Approach. In: Idoumghar, L., Legrand, P., Liefooghe, A., Lutton, E., Monmarché, N., Schoenauer, M. (eds) Artificial Evolution. EA 2019. Lecture Notes in Computer Science(), vol 12052. Springer, Cham. https://doi.org/10.1007/978-3-030-45715-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-45715-0_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45714-3
Online ISBN: 978-3-030-45715-0
eBook Packages: Computer ScienceComputer Science (R0)