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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic

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Author(s):
Pereira, Danillo Roberto [1] ; Papa, Joao Paulo [2] ; Rosalin Saraiva, Gustavo Francisco [1] ; Souza, Gustavo Maia [3]
Total Authors: 4
Affiliation:
[1] Univ Oeste Paulista, Sao Paulo - Brazil
[2] Univ Estadual Paulista, Sao Paulo - Brazil
[3] Univ Fed Pelotas, Pelotas, RS - Brazil
Total Affiliations: 3
Document type: Journal article
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE; v. 145, p. 35-42, FEB 2018.
Web of Science Citations: 6
Abstract

In plants, there are different types of electrical signals involving changes in membrane potentials that could encode electrical information related to physiological states when plants are stimulated by different environmental conditions. A previous study analyzing traits of the dynamics of whole plant low-voltage electrical showed, for instance, that some specific frequencies that can be observed on plants growing under undisturbed conditions disappear after stress-like environments, such as cold, low light and osmotic stimuli. In this paper, we propose to test different methods of automatic classification in order to identify when different environmental cues cause specific changes in the electrical signals of plants. In order to verify such hypothesis, we used machine learning algorithms (Artificial Neural Networks, Convolutional Neural Network, Optimum-Path Forest, k-Nearest Neighbors and Support Vector Machine) together Interval Arithmetic. The results indicated that Interval Arithmetic and supervised classifiers are more suitable than deep learning techniques, showing promising results towards such research area. (AU)

FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
Grantee:João Paulo Papa
Support Opportunities: Regular Research Grants
FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
Grantee:João Paulo Papa
Support Opportunities: Regular Research Grants