Prediction of non-linear time-variant dynamic crop model using Bayesian methods

M. Mansouri, B. Dumont, M. F. Destain

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This work addresses the problem of predicting a non-linear time-variant leaf area index and soil moisture model (LSM) using state estimation. These techniques include the extended Kalman filter (EKF), particle filter (PF) and the more recently developed technique, variational filter (VF). In the comparative study, the state variables (the leaf-area index LAI, the volumetric water content of the layer 1, HUR1 and the volumetric water content of the layer 2, HUR2) are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error with respect to the noise-free data. The results show that VF provides a significant improvement over EKF and PF.

Original languageEnglish
Title of host publicationPrecision Agriculture 2013 - Papers Presented at the 9th European Conference on Precision Agriculture, ECPA 2013
Pages507-513
Number of pages7
Publication statusPublished - 1 Dec 2013
Event9th European Conference on Precision Agriculture, ECPA 2013 - Lleida, Catalonia, Spain
Duration: 7 Jul 201311 Jul 2013

Publication series

NamePrecision Agriculture 2013 - Papers Presented at the 9th European Conference on Precision Agriculture, ECPA 2013

Other

Other9th European Conference on Precision Agriculture, ECPA 2013
CountrySpain
CityLleida, Catalonia
Period7/7/1311/7/13

    Fingerprint

Keywords

  • Crop model
  • Extended Kalman filter
  • LAI
  • Particle filter
  • Soil moisture prediction
  • Variational filter

ASJC Scopus subject areas

  • Agronomy and Crop Science

Cite this

Mansouri, M., Dumont, B., & Destain, M. F. (2013). Prediction of non-linear time-variant dynamic crop model using Bayesian methods. In Precision Agriculture 2013 - Papers Presented at the 9th European Conference on Precision Agriculture, ECPA 2013 (pp. 507-513). (Precision Agriculture 2013 - Papers Presented at the 9th European Conference on Precision Agriculture, ECPA 2013).