This thesis is about an exploration in the world of machine learning to find a way to predict the health state of a plant, in particular tobacco plants. The prediction is made possible by using an intrinsic measurement of the plant, the electrical impedance, and the surrounding environment, such as temperature, air humidity, soil moisture, ambient light, and the time when the measurements are taken. Predicting the plant status by knowing stem impedance, module and phase, and the environmental measurements can be seen as a classification problem. Therefore, the most suitable machine learning algorithms are the supervised learning ones. The most common supervised learning algorithms are the k-Nearest neighbor algorithm, Decision Tree, logistic regression, Support Vector Machine, and Neural Networks.