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

Chatterjee, Shre Kumar; Ghosh, Sanmitra; Das, Saptarshi; Manzella, V.; Vitaletti, Andrea; Masi, Elisa; Santopolo, Luisa; Mancuso, Stefano; Maharatna, Koushik

doi:10.1016/j.measurement.2014.03.040

Cited by 40 publications

(37 citation statements)

References 33 publications

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“…One initial attempt in this direction is provided by Chatterjee et al, where the authors measured and systemized 19 unique plant responses to light conditions. They further built a mathematical model to describe the relationship between light as an environmental stimulus and the electrical response as the measured output for a bay leaf ( Laurus nobilis ) plant ( Figure a) …”

Section: Plant‐based Sensorsmentioning

confidence: 99%

The Emergence of Plant Nanobionics and Living Plants as Technology

Lew

Koman

Gordiichuk

et al. 2019

Adv Materials Technologies

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Plants are naturally abundant and display high sensitivity to ecological factors to thrive in diverse environmental conditions. As sessile organisms, they have evolved complex, internal, and interplant signaling pathways with distinct structures to promptly adjust to the constantly changing environment. In the past five years, the unique ways in which they exchange information with and function in the environment have inspired an emerging field of plant nanobionics, which describes the interface between living plants and nanotechnology to impart the former with novel and useful functions. The structural merits of plant organs and organelles have also inspired the creation of plant‐derived structures through biointerfacing with nanoparticles containing electronic and optical properties. Here, the emerging applications and vision of plant nanobionics are highlighted together with related plant‐inspired materials in potentially replacing the myriad devices in the everyday lives stamped out of plastic, containing circuit boards and consuming power from the electrical grid. Applications in environmental sensing, communication devices, and energy harvesting and conversion are comprehensively discussed.

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Section: Plant‐based Sensorsmentioning

confidence: 99%

The Emergence of Plant Nanobionics and Living Plants as Technology

Lew

Koman

Gordiichuk

et al. 2019

Adv Materials Technologies

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“…Using the five classifiers, then the average accuracy of classification was 70%, and the best individual accuracy was 73.67% [47]. The forward and inverse modelling approaches for prediction of light stimulus from electrophysiological response in plants were established by Chatterjee and his coworkers [48].…”

Section: Plant Electrical Signal Recognition and Classificationmentioning

confidence: 99%

Plant Electrical Signal Classification Based on Waveform Similarity

et al. 2016

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(1) Background: Plant electrical signals are important physiological traits which reflect plant physiological state. As a kind of phenotypic data, plant action potential (AP) evoked by external stimuli-e.g., electrical stimulation, environmental stress-may be associated with inhibition of gene expression related to stress tolerance. However, plant AP is a response to environment changes and full of variability. It is an aperiodic signal with refractory period, discontinuity, noise, and artifacts. In consequence, there are still challenges to automatically recognize and classify plant AP; (2) Methods: Therefore, we proposed an AP recognition algorithm based on dynamic difference threshold to extract all waveforms similar to AP. Next, an incremental template matching algorithm was used to classify the AP and non-AP waveforms; (3) Results: Experiment results indicated that the template matching algorithm achieved a classification rate of 96.0%, and it was superior to backpropagation artificial neural networks (BP-ANNs), supported vector machine (SVM) and deep learning method; (4) Conclusion: These findings imply that the proposed methods are likely to expand possibilities for rapidly recognizing and classifying plant action potentials in the database in the future.

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“…Naturally, the model is also a representative of several practical contexts, such as muscle fiber dynamics [8] and electrophysiological response of plants [9] in biology, control of a distillation plant in industry [10], identification of core temperature in nuclear reactors [11], and human tracking in computer vision [12].…”

Section: Introductionmentioning

confidence: 99%