Machine Learning has application in a wide number of areas. Some of these applications are causing a revolution in numerous fields of science. In this paper, we used ML classifiers to prove that is possible identify the plant stress before the physical signs such as leaf fall, reduced productivity and others. In order to verify this hypothesis, we used different types of electrical signals that can be very useful to identify environmental conditions. In this context, we showed that some specific frequencies can be observed on ideal-condition plants, but disappearing after stress-environments, such as cold, low-light and osmotic conditions.
A very common task in the science is hypothesis test, generally the researchers use statistical tools to prove a hypothesis, however in the last years the Machine Learning algorithms is being used for this purpose. In this work we use ML classifiers to evaluate genotoxic effects in rural workers who were exposed to cigarette smoke and/or pesticides to identify possible classification patterns in the exposure groups. All of the algorithms displayed an excellent classification (accuracy > 80%).