Precision agriculture is a farming system based on the combination of detailed observations, measuring and rapid response to optimize energetic input to maximize crops production. Precision agriculture use decision support system (DSS) for optimize farm management. In this context, OPI (provide full name the first time) is an Intelligent Support System for precision agriculture. A vast set of data (i.e. temperature, relative humidity, deficit of vapour pressure, leaf wetness, solar radiation, carbon dioxide concentration, soil moisture etc.) is continuously collected, submitted to a local control unit, and processed through algorithms specifically developed for different crops. On the other hands, farmers can access OPI from their pc and mobile devices, and monitor complex agronomic data analysis presented in a user-friendly interface.

In our exposition, we will show how OPI works, and how its output can be used to assess the health state of plants through a specific set of functions. Moreover, we will show the methodology to develop useful predictive models based on this information. Specifically, we will describe a predictive algorithms capable to predict the infection risks of downy mildew for baby leaves plantations and for Fusarium ear blight of wheat.