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.
翻译:水稻是世界许多地区的主要主食之一。对水稻内核的质量估计在食品安全和社会经济影响方面都至关重要。这通常是过去由质量检查员进行的,这可能导致客观和主观的不准确性。在本文中,我们提出了一个水稻内核的自动视觉质量估计系统,根据水稻内核的缺陷类型对抽样水稻内核进行分类,并通过视界内核的重量比率来评估其质量。为了弥补不同内核数字的不平衡,并准确对有多重缺陷的内核进行分类,我们提出了一个多阶段工作流程,以便能够在所捕捉到的图像中找到内核,并对其属性进行分类。我们定义了一种新颖的衡量标准,以测量该地区每个内核内核的相对重量,从而可以计算每一种内核与所有样品的相对重量,并用作水稻质量估计的基础。我们进行了各种实验,以表明我们的系统能够以不接触的方式输出准确的结果,并取代易出易出错的手。