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In agronomics, predicting crop yield at a per field/county granularity isimportant for farmers to minimize uncertainty and plan seeding for the nextcrop cycle. While state-of-the-art prediction techniques employ graphconvolutional nets (GCN) to predict future crop yields given relevant featuresand crop yields of previous years, a dense underlying graph kernel requireslong training and execution time. In this paper, we propose a graphsparsification method based on the Fiedler number to remove edges from acomplete graph kernel, in order to lower the complexity of GCNtraining/execution. Specifically, we first show that greedily removing an edgeat a time that induces the minimal change in the second eigenvalue leads to asparse graph with good GCN performance. We then propose a fast method to choosean edge for removal per iteration based on an eigenvalue perturbation theorem.Experiments show that our Fiedler-based method produces a sparse graph withgood GCN performance compared to other graph sparsification schemes in cropyield prediction.

Graph Sparsification for GCN Towards Optimal Crop Yield Predictions