Abstract:In agronomics, predicting crop yield at a per field / county granularity is important for farmers to minimize uncertainty and plan seeding for the next crop cycle. While state-ofthe-art prediction techniques employ graph convolutional nets (GCN) to predict future crop yields given relevant features and crop yields of previous years, a dense underlying graph kernel requires long training and execution time. In this paper, we propose a graph sparsification method based on the Fiedler number to remove edges from … Show more
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