Machine vision based quality analysis of rice grains

Abstract

It is great challenge to meet the needs of quality assessment on rice grains. Testing on quality is gaining importance in food industry for classifying and grading the grains. Since manual testing is time consuming, costly and inaccurate, machine vision based quality analysis of rice grains is preferred. In machine vision based testing, we take both physical (grain shape and size) and chemical characteristics (amylose content, gel consistency) for evaluation and grading of rice grains. Quality assessment is done by finding 1) the region of boundary and 2) the end points of each grain by measuring the length, breadth and diagonal size of grain. In this proposed image processing algorithm, quality and grading of rice grains were analysed using the average values of the features extracted and it was implemented in Mat Lab.