In agriculture, plant cultivation requires to take numerous decisions. One of the major problems is the irrigation: a proper irrigation decision has to be made accordingly to the hydric state of the plant and the soil, and the weather prediction. In precision agronomy, this leads to use hydric sensors combined with a numerical model of 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 problem 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.