Rice quality assessment using fluorescence imaging technique

Grain rice quality is defined by various parameters including physical, biochemical and physiochemical properties. Most of the technology that have been developed only measure one quality characteristics at one time. More capability such as assessment of multiple quality parameters in one system is desirable. In this research, a machine vision with double lighting system has been developed to incorporate more features for the quality evaluation. The proposed machine vision system has the capability to obtain information related with morphological features and fluorescence color information of the rice simultaneously. Those features extracted from the image set were used to separate between non-white core, white core, chalky and dead sake rice with different freshness condition. This system shows promising result for separating difference type of rice with different freshness. These results provide an alternative way for quality grading technology for rice.

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