Prediction becomes a very important stage in many activities. In case of expectation crops of chosen agricultural foetuses we deal with a number of stimuli which consequently transform into the end effect. It is clear that the quality of those predictions has a great influence on subsequent stages in the production and distribution chain of agricultural foetuses. Neural networks by time series are a sophisticated technique of modeling capable of reflecting very complex functions. In time series problems, the objective is to predict ahead the value of a variable which varies in time, using previous values of that and/or other variables. The time series training data set therefore typically has a single variable, and this has type input/output (i.e., it is used both for network input and network output).