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Forward and inverse modelling approaches for prediction of light stimulus from electrophysiological response in plants

Forward and inverse modelling approaches for prediction of light stimulus from electrophysiological response in plants
Forward and inverse modelling approaches for prediction of light stimulus from electrophysiological response in plants
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.
Dynamical modelling, Environment prediction, Inverse model, Plant electrical signal, Statistical estimators, System identification
101-116
Chatterjee, Shre Kumar
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Ghosh, Sanmitra
012cd6b2-63ac-4821-afe3-9b208ba6fd82
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Manzella, Veronica
6bb669fd-5840-4288-b8fe-cd1f8abed5c2
Vitaletti, Andrea
c3fd5ffa-d2eb-4199-9e28-83b5a563e324
Masi, Elisa
f63b0e64-1daf-4c10-a8a5-01823b505d51
Santopolo, Luisa
7ed5bc8a-4166-40c7-9d44-de9991e3fe7a
Mancuso, Stefano
e9925eea-3fd7-418f-8f30-783f395000a1
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Chatterjee, Shre Kumar
aaa84ab8-3968-42b1-a9e1-d2a2e03c7b0a
Ghosh, Sanmitra
012cd6b2-63ac-4821-afe3-9b208ba6fd82
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Manzella, Veronica
6bb669fd-5840-4288-b8fe-cd1f8abed5c2
Vitaletti, Andrea
c3fd5ffa-d2eb-4199-9e28-83b5a563e324
Masi, Elisa
f63b0e64-1daf-4c10-a8a5-01823b505d51
Santopolo, Luisa
7ed5bc8a-4166-40c7-9d44-de9991e3fe7a
Mancuso, Stefano
e9925eea-3fd7-418f-8f30-783f395000a1
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd

Chatterjee, Shre Kumar, Ghosh, Sanmitra, Das, Saptarshi, Manzella, Veronica, Vitaletti, Andrea, Masi, Elisa, Santopolo, Luisa, Mancuso, Stefano and Maharatna, Koushik (2014) Forward and inverse modelling approaches for prediction of light stimulus from electrophysiological response in plants. Measurement, 53, 101-116. (doi:10.1016/j.measurement.2014.03.040).

Record type: Article

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 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.

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Accepted/In Press date: 25 March 2014
e-pub ahead of print date: 4 April 2014
Published date: July 2014
Keywords: Dynamical modelling, Environment prediction, Inverse model, Plant electrical signal, Statistical estimators, System identification
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 364861
URI: https://eprints.soton.ac.uk/id/eprint/364861
PURE UUID: f4af7b3f-061c-4883-beb1-dee8066300f7

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Date deposited: 30 Jan 2015 16:50
Last modified: 21 Oct 2019 19:21

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Contributors

Author: Sanmitra Ghosh
Author: Saptarshi Das
Author: Veronica Manzella
Author: Andrea Vitaletti
Author: Elisa Masi
Author: Luisa Santopolo
Author: Stefano Mancuso
Author: Koushik Maharatna

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