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. © 2014 Elsevier B.V. All rights reserved.

Forward and inverse modelling approaches for prediction of light stimulus from electrophysiological response in plants / Shre Kumar, Chatterjee; Sanmitra, Ghosh; Saptarshi, Das; Veronica, Manzella; Vitaletti, Andrea; Elisa, Masi; Luisa, Santopolo; Stefano, Mancuso; Koushik, Maharatna. - In: MEASUREMENT. - ISSN 0263-2241. - 53:(2014), pp. 101-116. [10.1016/j.measurement.2014.03.040]

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

VITALETTI, Andrea;
2014

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. © 2014 Elsevier B.V. All rights reserved.
2014
inverse model; dynamical modelling; system identification; environment prediction; plant electrical signal; statistical estimators
01 Pubblicazione su rivista::01a Articolo in rivista
Forward and inverse modelling approaches for prediction of light stimulus from electrophysiological response in plants / Shre Kumar, Chatterjee; Sanmitra, Ghosh; Saptarshi, Das; Veronica, Manzella; Vitaletti, Andrea; Elisa, Masi; Luisa, Santopolo; Stefano, Mancuso; Koushik, Maharatna. - In: MEASUREMENT. - ISSN 0263-2241. - 53:(2014), pp. 101-116. [10.1016/j.measurement.2014.03.040]
File allegati a questo prodotto
File Dimensione Formato  
VE_2014_11573-559014.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 4.7 MB
Formato Adobe PDF
4.7 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/559014
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 40
  • ???jsp.display-item.citation.isi??? 34
social impact