Parameter identification of plant growth models with stochastic development

Plant architectures generally display structural variations among individuals. Stochastic FSPMs have been developed to capture such feature, but calibrating such models is a challenging issue. For GreenLab model, parameter identification has been achieved on several crops and trees, but the estimation of functional parameters is mostly limited to plants with deterministic development. In this work, we propose a methodological framework allowing the efficient FSPM parameter estimation for stochastic ramified plants. We focus on the randomness in three kinds of meristem activities in plant development: growth, death and branching. Concepts of organic series and potential structure are introduced to build the fitting target as well as corresponding model output. We show that, with a limited set of sampled plants (here from simulation), using a few organic series, the inverse method retrieves well the parameter values (the original parameter set being known here). Requiring the concept of physiological age and the assumption of common biomass pool, the proposed approach provides a solution of solving source-sink functions of complex plant architectures, with a novel simplified way of plant sampling. The proposed parameter estimation frame is promising, since this in silico process mimics the procedure of calibrating model for real plants in a stand. Estimating parameters on stochastic plant architectures opens a new range of coming applications.