Classification and Segmentation of Watermelon in Images Obtained by Unmanned Aerial Vehicle

A. Ekiz, S. Arıca, A. Bozdogan
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引用次数: 1

Abstract

In this study, watermelons in the images obtained by an unmanned aerial vehicle (UAV) from watermelon field in Adana, Turkey, were segmented and classified. The original image obtained was processed in two ways. To start with, images were divided into overlapping blocks and gray level co-occurrence matrix (GLCM) from these blocks was generated and texture features were extracted using these GLCMs. Then, blocks containing or not containing watermelon were classified by employing a linear classifier. As a result of this study, the accuracy of watermelon classification was obtained as 86.46%. Second, k-means clustering of the original image was performed. Following this, groups having the highest blue value at its center were chosen. Finally, first and second results were combined with logical and operator. It was derived from this study that the method may be useful for detecting watermelons in field images obtained via UAV for counting and yield estimation.
无人机图像中西瓜的分类与分割
本研究对土耳其Adana西瓜田无人机图像中的西瓜进行了分割和分类。对得到的原始图像进行两种处理。首先,将图像划分为重叠块,生成灰度共生矩阵(GLCM),利用灰度共生矩阵提取纹理特征;然后,采用线性分类器对含有或不含有西瓜的块进行分类。研究结果表明,西瓜的分类准确率为86.46%。其次,对原始图像进行k-means聚类。在此之后,选择在其中心具有最高蓝色值的组。最后,将第一和第二结果与逻辑和运算符结合起来。本研究结果表明,该方法可用于无人机获取的西瓜田间图像的检测,用于西瓜的计数和产量估计。
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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