Vision-Based Defect Classification and Weight Estimation of Rice Kernels

TL;DR

This paper introduces an automated visual system for classifying rice kernels by flaw types and estimating their quality through weight ratios, improving accuracy and efficiency over manual inspection.

Contribution

The paper presents a novel multi-stage workflow and a new metric for contactless rice quality assessment based on visual analysis.

Findings

Accurate classification of rice kernels by flaw types.

Reliable estimation of kernel weights from images.

System outperforms manual inspection in precision and speed.

Abstract

Rice is one of the main staple food in many areas of the world. The quality estimation of rice kernels are crucial in terms of both food safety and socio-economic impact. This was usually carried out by quality inspectors in the past, which may result in both objective and subjective inaccuracies. In this paper, we present an automatic visual quality estimation system of rice kernels, to classify the sampled rice kernels according to their types of flaws, and evaluate their quality via the weight ratios of the perspective kernel types. To compensate for the imbalance of different kernel numbers and classify kernels with multiple flaws accurately, we propose a multi-stage workflow which is able to locate the kernels in the captured image and classify their properties. We define a novel metric to measure the relative weight of each kernel in the image from its area, such that the relative weight of each type of kernels with regard to the all samples can be computed and used as the basis for rice quality estimation. Various experiments are carried out to show that our system is able to output precise results in a contactless way and replace tedious and error-prone manual works.