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首页> 外文期刊>International Journal of Computer Science Engineering and Information Technology Research >AUTOMIZATION OF AGRICULTURE PRODUCTS DEFECT DETECTION AND GRADING USING IMAGE PROCESSING SYSTEM
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AUTOMIZATION OF AGRICULTURE PRODUCTS DEFECT DETECTION AND GRADING USING IMAGE PROCESSING SYSTEM

机译：利用图像处理系统实现农产品缺陷检测与分级的自动化

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Grading of fruit is important phase after harvesting and before marketing. The automatic fruit grading system extracts its defective region and grading, according to its level of defection. The classification of Fruit is done into four categories by considering smaller changes in defective parts, so it increases output efficiency and user acceptance level of different fruit categories.In this proposed system of image processing algorithm, rgb2gray method and median filter are used to pre-processed the input image and convert it into gray scaled image; after that input image is segmented using modern iterative tri-class threshold based on Otsu 's method for classifications in terms of defect, extract statistical features texture and shape, with the normalized Symmetric GLCM method. For testing purpose, Apple images data collected from the database provided by Mechanics and Construction Department of Gem-bloux Agricultural University of Belgium [8] have been used. Fruits are classified into four categories by using kNN method with 95% accuracy (Category I, Category 2, Category 3 and Category 4).
机译：水果分级是收获后和销售前的重要阶段。自动水果分级系统根据缺陷程度提取缺陷区域并进行分级。考虑到缺陷部分的较小变化，将水果分类为四类，从而提高了不同水果类别的输出效率和用户接受度。在此图像处理算法系统中，使用rgb2gray方法和中值滤波器进行预处理输入图像并将其转换为灰度图像；然后使用基于Otsu的方法对缺陷进行分类的现代迭代三级阈值对输入图像进行分割，并使用归一化对称GLCM方法提取统计特征纹理和形状。出于测试目的，使用了从比利时Gem-bloux农业大学的机械和建筑系提供的数据库中收集的Apple图像数据[8]。使用kNN方法将水果分为95类（I类，2类，3类和4类），将其分为四类。

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