Maximum Expectation algorithm and neuronal network base radial applied to the estimate of an environmental variable, evapotranspiration in a greenhouse

This article analyzes the data mining techniques to get the evapotranspiration variable (ETo) in order to control the irrigation system in green houses to optimize resources such as water and fertilizers. The methods used are the Maximum Expectation algorithm (EM) and the neuronal network base radial; such methods estimate the environmental variable from historical data such as: the temperature and moisture values collected from the sensors that are within the green house. These methods predict values from statistical distribution that give us the optimum value for temperature and moisture values occurring at that moment. This data will be compared to determined values for the formulas of the Penman-Monteith model, which has been until now the model with more reliable results.