Journal
MEASUREMENT
Volume 53, Issue -, Pages 101-116Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2014.03.040
Keywords
Dynamical modelling; Environment prediction; Inverse model; Plant electrical signal; Statistical estimators; System identification
Funding
- PLants Employed As SEnsor Devices (PLEASED)
- EC [296582]
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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. (C) 2014 Elsevier Ltd. All rights reserved.
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