  Advances in remote sensing technology have led to the capture of massive
amounts of data. Increased image resolution, more frequent revisit times, and
additional spectral channels have created an explosion in the amount of data
that is available to provide analyses and intelligence across domains,
including agriculture. However, the processing of this data comes with a cost
in terms of computation time and money, both of which must be considered when
the goal of an algorithm is to provide real-time intelligence to improve
efficiencies. Specifically, we seek to identify nutrient deficient areas from
remotely sensed data to alert farmers to regions that require attention;
detection of nutrient deficient areas is a key task in precision agriculture as
farmers must quickly respond to struggling areas to protect their harvests.
Past methods have focused on pixel-level classification (i.e. semantic
segmentation) of the field to achieve these tasks, often using deep learning
models with tens-of-millions of parameters. In contrast, we propose a much
lighter graph-based method to perform node-based classification. We first use
Simple Linear Iterative Cluster (SLIC) to produce superpixels across the field.
Then, to perform segmentation across the non-Euclidean domain of superpixels,
we leverage a Graph Convolutional Neural Network (GCN). This model has
4-orders-of-magnitude fewer parameters than a CNN model and trains in a matter
of minutes.
