Inspection and classification of wheat quality using image processing

Junsong Zhu, Baosheng Sun, Jianrong Cai, Yongjian Xu, Feng Lu, Haile Ma
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Abstract

Wheat plays an important role in our daily life and industrial production. Several computer vision approaches have been proposed for classifying wheat quality, but there were some methods focusing on the problem of cohesive wheats while image processing. In this paper, we designed a single kernel guide groove to separate the cohesive wheats, which could simplify the algorithm of image processing and improve the accuracy rate of classification. For the method followed while recording the data, the image information must be converted into digital information, and the results are provided using appropriate image processing algorithms. Image preprocessing steps such as binarization, image enhancement, image segmentation, and morphological processing were used to reduce noise. For image segmentation, we proposed the following new segmentation methods: (1) extracting wheat region by converting image to H channel and (2) watershed algorithm based on Euclidean distance transformation. For the classification model, 22 features of 7 different qualities of wheat were inputted in the Back Propagation (BP) neural network and Support Vector Machine (SVM) model, and the overall correct classification rates were determined to be 91% and 97% for SVM and BP neural network, respectively. The BP neural network was more suitable for wheat appearance quality detection.
基于图像处理的小麦品质检测与分类
小麦在我们的日常生活和工业生产中起着重要的作用。目前已经提出了几种用于小麦品质分类的计算机视觉方法,但有一些方法主要针对图像处理过程中小麦的内聚性问题。本文设计了一种单粒导向槽来分离黏聚小麦,简化了图像处理算法,提高了分类准确率。对于记录数据时所遵循的方法,必须将图像信息转换为数字信息,并使用适当的图像处理算法提供结果。图像预处理步骤,如二值化,图像增强,图像分割和形态学处理,以减少噪声。在图像分割方面,我们提出了以下新的分割方法:(1)将图像转换为H通道提取小麦区域;(2)基于欧几里得距离变换的分水岭算法。在分类模型中,将7种小麦品质的22个特征输入到BP神经网络和支持向量机(SVM)模型中,确定SVM和BP神经网络的分类正确率分别为91%和97%。BP神经网络更适合于小麦外观品质的检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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