Separation of abnormal regions on black gram leaves using image analysis

Pavit Noinongyao, U. Watchareeruetai, Puriwat Khantiviriya, Chaiwat Wattanapaiboonsuk, S. Duangsrisai
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引用次数: 8

Abstract

This paper proposes an image analysis method for separating abnormal regions caused by nutrient deficiencies on plants' leaves. The proposed method analyzes a histogram of normal leaves' colors to identify abnormalities on leaves. It can be divided into three main steps. Firstly, color features of leaf region in an input image are computed. Secondly, for each pixel, its color features are compared to the corresponding bin in the histogram to determine whether the pixel is abnormal. Finally, a post-processing technique is then applied to reduce noises in the result. Experiments have been conducted using black gram (Vigna mungo) leaves with five different nutrient deficiencies. The experimental results show that the proposed method can separate abnormal regions with an accuracy of above 90%.
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利用图像分析方法分离黑克兰叶片异常区域
提出了一种分离植物叶片营养缺乏异常区域的图像分析方法。该方法通过分析正常叶片颜色的直方图来识别叶片上的异常。它可以分为三个主要步骤。首先,计算输入图像中叶子区域的颜色特征;其次,对于每个像素,将其颜色特征与直方图中对应的bin进行比较,判断像素是否异常。最后,应用后处理技术来降低结果中的噪声。用五种不同营养缺乏的黑克兰(Vigna mungo)叶片进行了试验。实验结果表明,该方法能有效地分离出异常区域,准确率达90%以上。
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