Bayesian Estimation for the GreenLab Plant Growth Model with Deterministic Organogenesis View Full Text


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Article Info

DATE

2021-08-18

AUTHORS

D. Logothetis, S. Malefaki, S. Trevezas, P.-H. Cournède

ABSTRACT

Plant growth modeling has attracted a lot of attention due to its potential applications. Many scientific disciplines are involved, and a lot of research effort and intensive computer methods were needed to understand better the complex mechanisms underlying plant evolution. Among the numerous challenges, one can cite mathematical modeling, parameterization, estimation and prediction. One of the most promising models that have been proposed in the literature is the GreenLab functional–structural plant growth model. In this study, we focus only on one of its versions, named GreenLab-1, particularly adapted to a certain class of plants with known organogenesis, such as sugar beet, maize, rapeseed and other crop plants. The parameters of the model are related to plant functioning, and the vector of observations consists of organ masses measured only once at a given observation time. Previous efforts for parameter estimation in GreenLab-1 include Kalman-type filters, stochastic variants of EM and/or ECM algorithms, and hybrid sequential importance sampling algorithms with Bayesian estimation only for the functional parameters of the model. In this paper, the first purely Bayesian approach for parameter estimation of the GreenLab-1 model is proposed. This approach has much more flexibility in handling complex structures, thus providing a useful tool for analyzing such types of models. In order to sample from the posterior distribution an MCMC algorithm is used and its implementation issues are also discussed. The performance of this method is illustrated on a simulated and a real dataset from the sugar beet plant, and a comparison is made with the MLE approach. More... »

PAGES

63-87

References to SciGraph publications

  • <error retrieving object. in <ERROR RETRIEVING OBJECT
  • 1998-12. Convergence assessment techniques for Markov chain Monte Carlo in STATISTICS AND COMPUTING
  • 2013-03-15. A Sequential Monte Carlo Approach for MLE in a Plant Growth Model in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 2015-03-07. A Regularized Particle Filter EM Algorithm Based on Gaussian Randomization with an Application to Plant Growth Modeling in METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY
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    http://scigraph.springernature.com/pub.10.1007/s13253-021-00468-w

    DOI

    http://dx.doi.org/10.1007/s13253-021-00468-w

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