One of the main problems organic banana crops is the presence of pests, affecting crop yield, post-harvest and export fruit quality. In Piura (Peru), pests with the greatest presence are Thrips, Squamas, Black Weevil, etc. This article describes the development of a prediction model, based on a supervised machine learning algorithm: Logistic Regression and Support Vector Machine, which will estimate the future level of incidence (low and medium) of a specific pest. The model was designed including the input data (climate) that were obtained from a network of IoT sensors in-situ in the banana crop, and output data (level of incidence) that was collected with manual record and visual inspection. The model developed can predict pest incidence at 79% accuracy (with test data). These first results show feasibility to estimate in advance the incidence of pests in that crop. Future implementati...