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

2018; Elsevier BV; Volume: 145; Linguagem: Inglês

10.1016/j.compag.2017.12.024

1872-7107

Danillo Roberto Pereira, João Paulo Papa, Gustavo Francisco Rosalin Saraiva, Gustavo Maia Souza,

Plant Parasitism and Resistance

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.