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

Pereira, Danillo Roberto; Papa, Joao Paulo [UNESP]; Rosalin Saraiva, Gustavo Francisco; Souza, Gustavo Maia

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

Arquivos

WOS000425577400005.pdf (1.29 MB)

Data

2018-02-01

Autores

Pereira, Danillo Roberto

Papa, Joao Paulo

Rosalin Saraiva, Gustavo Francisco

Souza, Gustavo Maia

Editor

Elsevier B.V.

Tipo

Artigo

Direito de acesso

Acesso aberto

Resumo

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.