Iterative convolution particle filtering for nonlinear parameter estimation and data assimilation with application to crop yield prediction

Abstract

The complexity of plant growth models and the scarcity of experimental data make the application of conventional data assimilation techniques rather difficult. In this paper, we use the Convolution Particle Filter (CPF) and an iterative adaptation, the Iterative Convolution Particle Filter (ICPF) for nonlinear parameter estimation. Both methods provide prior distributions in the Bayesian framework for data assimilation. CPF is sequentially used to update state and parameter estimates in order to improve model prediction and to assess the predictive uncertainty. The predictive performances of the two methods are evaluated by an application to the LNAS sugar beet growth model with three sets of real measurements, one used for parameter estimation and the two others used to test the model predictive capacity, both with and without data assimilation. Despite the low accuracy and the scarcity of the early data used for assimilation, the CPFbased data assimilation approach with the prior distribution based on ICPF estimations showed promising predictive capacities and provided robust confidence intervals. The method can therefore be considered as a potential candidate for yield prediction applications in agriculture.

Towards an EDSL to Enhance Good Modelling Practice for Non-linear Stochastic Discrete Dynamical Models - Application to Plant Growth Models

“…The following results are based on the CPF method applied to the Log Normal Allocation and Senescence (LNAS) daily crop model with real experimental data (Chen et al, 2013). The equations of the LNAS model are derived for sugar beet with three main processes during the plant growth period: biomass production, allocation and senescence.…”