Vision-Based Defect Classification and Weight Estimation of Rice Kernels
- URL: http://arxiv.org/abs/2210.02665v1
- Date: Thu, 6 Oct 2022 03:58:05 GMT
- Title: Vision-Based Defect Classification and Weight Estimation of Rice Kernels
- Authors: Xiang Wang, Kai Wang, Xiaohong Li, Shiguo Lian
- Abstract summary: 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.
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.
- Score: 12.747541089354538
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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