Rice Classification Using Spatio-Spectral Deep Convolutional Neural Network

TL;DR

This paper presents a non-destructive rice classification system that combines hyperspectral imaging with deep CNNs to automatically extract features, significantly improving accuracy over traditional methods.

Contribution

The study introduces a novel spatio-spectral deep CNN approach for rice classification that eliminates the need for hand-engineered features, enhancing accuracy and efficiency.

Findings

Achieved up to 11.9% accuracy improvement over support vector machine methods

Demonstrated effectiveness on two rice datasets

Validated the synergy of hyperspectral imaging and deep learning for rice inspection

Abstract

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.