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Clustering analysis of Coleopteran stored product pest based on morphometric structure

机译:基于形态学结构的鞘翅目储存产品害虫的聚类分析

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The Coleopteran stored product pest contribute severe damage to stored product. Therefore, the identification of the insect pest is crucial step in the pest management program. However, the abundant of insect pest's species may cause the difficulty in the identification process specially when using morphological image and molecular techniques. In this paper, the identification of the insect pest species is obtained using statistical analysis which are K-means clustering and Hierarchical Agglomerative Cluster Analysis (HACA). Based on the morphometric analysis of four morphological structure of 38 Coleopteran stored product pest species image, 100 dataset is generated. As a results, from two different clustering techniques, K-Means Clustering and Hierarchical Agglomerative Cluster Analysis (HACA) produce 5 clusters and 11 clusters, respectively. From the clustering evaluation, it is show that the HACA is the best since it produce the higher average Silhouette index.
机译:植物植物储存的产品害虫对储存产品产生严重损害。因此,昆虫害虫的鉴定是害虫管理计划中的关键步骤。然而,在使用形态学图像和分子技术时,昆虫害虫的物种的丰富可能会造成识别过程中的难度。在本文中,使用统计分析获得昆虫生物物种的鉴定,其是K-Means聚类和分级凝聚聚类分析(Haca)。基于38个鞘翅目储存产品害虫物种图像的四种形态结构的形态学分析,产生了100个数据集。作为结果,从两种不同的聚类技术,K-Means聚类和分层附聚类聚类分析(HACA)分别产生5个簇和11个簇。从聚类评估中,显示HACA是最好的,因为它产生了更高的平均轮廓索引。

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