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Predicting Grain Protein Content of Winter Wheat

The objective of this paper is to propose to use a new Improved Particle Filtering (IPF) based on minimizing Kullback-Leibler divergence for crop models' predictions. The performances of the method are compared with those of the conventional Particle Filtering (PF) at a complex crop model (AZODYN) to predict an important winter-wheat quality criterion, namely the grain protein content. Furthermore, the effect of measurement noise (e.g., different signal-to-noise ratios) on the performances of PF and IPF is investigated. The results of the comparative studies show that the IPF provides a significant improvement over the PF because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. The efficiency of IPF is expressed in terms of estimation accuracy (root mean square error).