Spatial-temporal Techniques for Prediction and Compression of Soil Fertility Data
نویسنده
چکیده
In this paper, we propose several techniques for data reduction and spatialtemporal prediction in precision agriculture databases. The proposed methods are based on various statistical and machine learning techniques including sensitivity based analysis, spatial-temporal autoregression, multiple time series and response modeling with spatially-correlated residuals. The considered techniques are implemented in described a prototype software and applied for analysis and compression of multi-temporal precision agriculture data. The spatial-temporal prediction on real-life soil fertility data using the proposed spatial-temporal autoregression method is discussed in an accompanying paper.
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