Forward and Inverse Modelling Approaches for Prediction of Light Stimulus from Electrophysiological Response in Plants
Chatterjee, SK; Ghosh, S; Das, S; et al.Manzella, V; Vitaletti, A; Masi, E; Santopolo, L; Mancuso, S; Maharatna, K
Date: 4 March 2014
Journal
Measurement
Publisher
Elsevier for International Measurement Confederation (IMEKO)
Publisher DOI
Abstract
In this paper, system identification approach has been adopted to develop a novel dynamical model for describing the relationship between light as an environmental stimulus and the electrical response as the measured output for a bay leaf (Laurus nobilis) plant. More specifically, the target is to predict the characteristics of the ...
In this paper, system identification approach has been adopted to develop a novel dynamical model for describing the relationship between light as an environmental stimulus and the electrical response as the measured output for a bay leaf (Laurus nobilis) plant. More specifically, the target is to predict the characteristics of the input light stimulus (in terms of on-off timing, duration and intensity) from the measured electrical response - leading to an inverse problem. We explored two major classes of system estimators to develop dynamical models - linear and nonlinear - and their several variants for establishing a forward and also an inverse relationship between the light stimulus and plant electrical response. The best class of models are given by the Nonlinear Hammerstein-Wiener (NLHW) estimator showing good data fitting results over other linear and nonlinear estimators in a statistical sense. Consequently, a few set of models using different functional variants of NLHW has been developed and their accuracy in detecting the on-off timing and intensity of the input light stimulus are compared for 19 independent plant datasets (including 2 additional species viz. Zamioculcas zamiifolia and Cucumis sativus) under similar experimental scenario.
Mathematics and Statistics
Faculty of Environment, Science and Economy
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