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WATERMELON PLANT CLASSIFICATION BASED ON SHAPE AND TEXTURE FEATURE LEAF USING SUPPORT VECTOR MACHINE (SVM)

机译:使用支持向量机(sVm)的基于形状和纹理特征叶的西瓜植物分类

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摘要

Nowadays, some efforts are used to increase results of agriculture production. One of those is utilizing herbisides to exterminate the weeds. However, there are some of the weeds having resemblance with the plant, with the result that we need to classify the plant and the weeds before utilizing herbisides as an extermination weeds. In this paper, we use watermelon plant classification as case study. The recognition of the plant owned by the similarity of leaves of these plants are divided into three phases. At the first phase we perform preprocessing to convert the RGB image into a grayscale images. Further, the grayscale images are changed into segmentation of edge detection using Canny operator. In the second, we use feature extraction to retrieve important informations for the recognition of those leaves. The last phase we classify that leaves as watermelon plants or weeds using Support Vector Machine (SVM) algorithm. The results of early trials indicate that this method has an accuracy of 91,3%. udKeywords : image, leaf, edge detection, feature extraction, and plant classification esults of early trials
机译:如今,人们为提高农业生产成果付出了一些努力。其中之一是利用除草剂消灭杂草。但是,有些杂草与植物相似,因此我们需要在使用除草剂作为灭草之前对植物和杂草进行分类。本文以西瓜植物分类为例。这些植物的叶片相似性所拥有的植物识别分为三个阶段。在第一阶段,我们执行预处理以将RGB图像转换为灰度图像。此外,使用Canny算子将灰度图像改变为边缘检测的分割。在第二篇文章中,我们使用特征提取来检索重要信息以识别那些叶子。在最后一个阶段,我们使用支持向量机(SVM)算法将叶子分类为西瓜植物或杂草。早期试验的结果表明,该方法的准确度为91.3%。 ud关键字:早期试验的图像,叶片,边缘检测,特征提取和植物分类结果

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