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.
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Notes
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Some parameters can also have no meaning from a biological point of view.
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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).
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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
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