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Identification using the K -Means Clustering and Gray Level Co-occurance Matrix (GLCM) At Maturity Fruit Oil Head

机译:在成熟果油压头处使用K均值聚类和灰度共生矩阵(GLCM)进行识别

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This study discusses the identification of its palm fruit camp picture image using K-Means Clustering and identification GLCM. Process fruits traditionally experienced constrained due to human nature that has flaws that the desired results are not effective. Advances in computer technology have come into the world in terms of the farm before harvest and post-harvest. This Dimolished how to recognize the fruit so that it correspond to real conditions. Condition of oil palm fruit is determined by the level of maturity in terms of color, texture and shape of the oil palm fruit. Identification which did classify in the category of mature and not mature. Determination of identification with the K-means clustering method that uses the difference in euclidean distance and GLCM feature extraction as a reference. For the results of the present study is equal to 90% of the 50 test data.
机译:这项研究讨论了使用K-Means聚类和GLCM识别对其棕榈果营图片图像的识别。由于人类的天性,传统上受制于加工结果的果实受到限制,存在缺陷,即预期结果无效。就收获前和收获后的农场而言,计算机技术的进步已席卷全球。这破坏了如何识别水果,使其与实际条件相对应。油棕果实的状况取决于油棕果实的颜色,质地和形状的成熟程度。确实分类为成熟和不成熟类别的标识。使用欧氏距离差异和GLCM特征提取的K均值聚类方法确定识别。对于本研究的结果等于50个测试数据的90%。

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