ArticleBiosensors2018
Chemical Sensing Employing Plant Electrical Signal Response-Classification of Stimuli Using Curve Fitting Coefficients as Features.
Article in Biosensors, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
16 citing papers in PubMed, 53 citations in OpenAlex.
- Silent Signals: Correlating Plant Bioelectric Activity with Human Emotional States via Wearable Sensing.Biomimetics (Basel, Switzerland) · 2026Article
- Analysis of Electrome as a Tool for Plant Monitoring: Progress and Perspectives.Plants (Basel, Switzerland) · 2025Review
- Metaheuristics Algorithm-Based Optimization for High Conductivity and Hardness CuNiMaterials (Basel, Switzerland) · 2025Article
- Early detection of dark-affected plant mechanical responses using enhanced electrical signals.Plant methods · 2024Article
- Effect of low-dose ionizing radiation on spatiotemporal parameters of functional responses induced by electrical signals in tobacco plants.Photosynthesis research · 2023Article
- Detecting stress caused by nitrogen deficit using deep learning techniques applied on plant electrophysiological data.Scientific reports · 2023Article
- Hyperpolarization electrical signals induced by local action of moderate heating influence photosynthetic light reactions in wheat plants.Frontiers in plant science · 2023Article
- Multiclass classification of environmental chemical stimuli from unbalanced plant electrophysiological data.PloS one · 2023Article
- Simulated Analysis of Influence of Changes in HPlants (Basel, Switzerland) · 2022Article
- Influence of Burning-Induced Electrical Signals on Photosynthesis in Pea Can Be Modified by Soil Water Shortage.Plants (Basel, Switzerland) · 2022Article
- Broadening the definition of a nervous system to better understand the evolution of plants and animals.Plant signaling & behavior · 2021Review
- Electrical Signals, Plant Tolerance to Actions of Stressors, and Programmed Cell Death: Is Interaction Possible?Plants (Basel, Switzerland) · 2021Review
- Stochastic Spatial Heterogeneity in Activities of HInternational journal of molecular sciences · 2021Review
- Influence of Local Burning on Difference Reflectance Indices Based on 400-700 nm Wavelengths in Leaves of Pea Seedlings.Plants (Basel, Switzerland) · 2021Article
- Exogenous Abscisic Acid Can Influence Photosynthetic Processes in Peas through a Decrease in Activity of HBiology · 2020Article
- Burning-induced electrical signals influence broadband reflectance indices and water index in pea leaves.Plant signaling & behavior · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to exploit plants as environmental biosensors, previous researches have been focused on the electrical signal response of the plants to different environmental stimuli. One of the important outcomes of those researches has been the extraction of meaningful features from the electrical signals and the use of such features for the classification of the stimuli which affected the plants. The classification results are dependent on the classifier algorithm used, features extracted and the quality of data. This paper presents an innovative way of extracting features from raw plant electrical signal response to classify the external stimuli which caused the plant to produce such a signal. A curve fitting approach in extracting features from the raw signal for classification of the applied stimuli has been adopted in this work, thereby evaluating whether the shape of the raw signal is dependent on the stimuli applied. Four types of curve fitting models-Polynomial, Gaussian, Fourier and Exponential, have been explored. The fitting accuracy (i.e., fitting of curve to the actual raw signal) depicted through R-squared values has allowed exploration of which curve fitting model performs best. The coefficients of the curve fit models were then used as features. Thereafter, using simple classification algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) etc. within the curve fit coefficient space, we have verified that within the available data, above 90% classification accuracy can be achieved. The successful hypothesis taken in this work will allow further research in implementing plants as environmental biosensors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.