  Rice has been one of the staple foods that contribute significantly to human
food supplies. Numerous rice varieties have been cultivated, imported, and
exported worldwide. Different rice varieties could be mixed during rice
production and trading. Rice impurities could damage the trust between rice
importers and exporters, calling for the need to develop a rice variety
inspection system. In this work, we develop a non-destructive rice variety
classification system that benefits from the synergy between hyperspectral
imaging and deep convolutional neural network (CNN). The proposed method uses a
hyperspectral imaging system to simultaneously acquire complementary spatial
and spectral information of rice seeds. The rice varieties are then determined
from the acquired spatio-spectral data using a deep CNN. As opposed to several
existing rice variety classification methods that require hand-engineered
features, the proposed method automatically extracts spatio-spectral features
from the raw sensor data. As demonstrated using two types of rice datasets, the
proposed method achieved up to 11.9% absolute improvement in the mean
classification accuracy, compared to the commonly used classification methods
based on support vector machines.
