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首页> 外文期刊>Postharvest Biology and Technology >Detection of early bruises on peaches (Amygdalus persica L.) using hyperspectral imaging coupled with improved watershed segmentation algorithm
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Detection of early bruises on peaches (Amygdalus persica L.) using hyperspectral imaging coupled with improved watershed segmentation algorithm

机译：使用高光谱成像与改进的流域分割算法耦合的桃子（Amygdalus Persica L.）的早期瘀伤检测

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Bruise is the most common type of damage to peaches in a major cause of quality loss. However, fast and nondestructive detection of early bruises on peaches is a challenging task. In this study, short-wave near infrared (SW-NIR) and long-wave near infrared (LW-NIR) hyperspectral imaging technologies were observed and compared the ability to discriminate bruised from sound regions. Principal components analysis (PCA) was utilized to select the effective wavelengths for each type of imaging mode. SW-NIR imaging mode was more suitable for detection of early bruises on peaches. A novel improved watershed segmentation algorithm based on morphological gradient reconstruction and marker extraction was developed and applied to the multispectral PC images. The detection results indicated that for all test peaches used in this experiment, 96.5% of the bruised and 97.5% of sound peaches were accurately identified, respectively. A proposed algorithm was superior to the common segmentation methods including Ostu and the global threshold value method. This study demonstrated that SW-NIR hyperspectral imaging coupled with the proposed improved watershed segmentation algorithm could be a potential approach for detection of early bruises on peaches.
机译：瘀伤是质量损失主要原因的桃子最常见的伤害。然而，对桃子的早期瘀伤的快速和无损检测是一个具有挑战性的任务。在本研究中，观察到近红外线（SW-NIR）和近红外线（LW-NIR）高光谱成像技术的短波，并比较了从声音区域区分瘀伤的能力。主要成分分析（PCA）用于为每种类型的成像模式选择有效波长。 SW-NIR成像模式更适合于在桃子上检测早期瘀伤。一种新颖的基于形态梯度重构和标记提取的流域分割算法，并应用于多光谱PC图像。检测结果表明，对于本实验中使用的所有测试桃，分别为96.5％的瘀伤和97.5％的声音桃子被精确鉴定。一个所提出的算法优于包括OSTU的公共分段方法和全局阈值方法。本研究证明，与所提出的改进的流域分割算法耦合的SW-NIR高光谱成像可能是用于检测桃子早期瘀伤的潜在方法。

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