A prototypical few-shot network with pre-computed class prototypes reaches 97.75 percent accuracy on eight grain types using 2,880 training images, close to the 99.75 percent of a model trained on 16,666 images.
Identification of wheat classes at different moisture levels using near-infrared hyperspectral images of bulk samples,
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Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data
A prototypical few-shot network with pre-computed class prototypes reaches 97.75 percent accuracy on eight grain types using 2,880 training images, close to the 99.75 percent of a model trained on 16,666 images.