OPTIMAL - cOPernicus irrigaTION mAnagement tooLkit - environmental parameters forecast demonstration
OPTIMAL for cOPernicus irrigaTION mAnagement tooLkit is a combination of state-of-the-art Machine Learning techniques that is designed to forecast environmental parameters which enable the delivery of irrigation needs intelligence.
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What is the challenge that is being addressed?
The challenge addressed is to forecast environmental parameters towards enabling the generation of irrigation forecasts.
What is the AI solution the project has implemented?
The model presented is based on a Long short-term memory model used to generate forecasts of the several environmental parameters while given 7 days of inputs and towards delivering 7 days of outputs.
This model enables the creation of a function which, by calibration of the environmental parameters and the irrigation needs of a given site, allows the generation of irrigation intelligence (e.g.: irrigation schedule). Detailled explanations and example data is provided in order to test and demonstrate the model.
Who helped implement the AI solution?
This solution is implemented in the context of OPTIMAL, a winning project from the AI4Copernicus 3rd Open Call, by the Xilbi Sistemas de Informacion SL company. The results of the project are available here.
