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Geospatial information covering satellite information plays a vital role as it can provide normal, predictable and target data. Distinguishing geospatial designs and measure changes that happen in space with time requires exceptional systems to be used. Different regular, monetary and organic variables impacts on the yield production of crop, yet random changes in these variables prompt loss to the farmers. These losses and various dangers/risks can be evaluated when proper numerical or measurable strategies are connected on information identified related to soil, climate and past year crop yield. This research work displays a review on various models that are utilized for forecasting crop yield production using statistical data and geo spatial data. This work uses spiking neural network model.

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