ABSTRACT
Plants can emit electrical signals under stress conditions, such as
predator attacks and climatic changes. The capture and
identification of these signals allows biologists and other
researchers to identify the cause of the stress and contribute to the
identification of external factors that may pose a risk to plant health.
This research aims to classify the presence of stress through the
electrical signals emitted by plants in response to different types of
stimuli, generated in controlled environments, using Machine
Learning techniques. Several algorithms are applied, and the results
are compared. Tree-based models show better responses in
identifying different types of stress in plants.