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首页> 外文期刊>Postharvest Biology and Technology >Detection of early decayed oranges based on multispectral principal component image combining both bi-dimensional empirical mode decomposition and watershed segmentation method
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Detection of early decayed oranges based on multispectral principal component image combining both bi-dimensional empirical mode decomposition and watershed segmentation method

机译：基于多光谱主成分图像的早期衰减橙子的检测组合双维经验模型分解和流域分割方法

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摘要

Detection of early decay caused by fungal infections in citrus fruit still remains one of the major problems in the post-harvest processing and automatic quality grading. A new combination algorithm by merging multispectral principal component image, bi-dimensional empirical mode decomposition and image reconstruction as well as improved watershed segmentation was proposed to detect the early decay in oranges. Segmented principal component analysis based on three wavelength regions including visible and short wavelength near-infrared (500-1050 nm), visible (500-780 nm) and near-infrared (781-1050 nm) was performed to determine the optimal principal component (PC) image that was used to extract the effective wavelength images by weighting coefficient analysis. Seven wavelength images in the spectral region of 500-1050 nm were finally determined to build the multispectral PC images. The bi-dimensional empirical mode decomposition (BEMD) was used to remove noise in the multispectral PC images and further reconstruct images. An improved watershed segmentation method with morphological gradient reconstruction, marker extraction as well as image amendment, was proposed to segment decay regions in fruit by using the reconstructed multispectral PC images. All samples including 220 each of decayed and sound fruit were utilized to assess classification ability of the proposed combination algorithm. The results indicated that identification accuracies of decayed and sound fruit were 97.3% and 100%, respectively. The multispectral principal component image combining both bi-dimensional empirical mode decomposition and watershed segmentation method can be used as an effective tool for detection of early decayed oranges, and it was also promising for development of a fast and low-cost multispectral imaging system.
机译：柑橘类水果真菌感染引起的早期衰变的检测仍然是收获后处理和自动质量分级的主要问题之一。通过合并多光谱主成分图像，双维经验模式分解和图像重建以及改进的流域分割的新组合算法，以检测橙子中的早期衰减。基于三个波长区域的分段主成分分析包括可见和短波长近红外（500-1050nm），可见（500-780nm）和近红外（781-1050nm）以确定最佳主成分（ PC）用于通过加权系数分析提取有效波长图像的图像。最终确定500-1050nm的频谱区域中的七个波长图像以构建多光谱PC图像。双维经验模式分解（BEMD）用于去除多光谱PC图像中的噪声并进一步重建图像。通过使用重建的多光谱PC图像，提出了一种改进的具有形态梯度重建，标记提取以及图像修正的分解方法，标记提取以及图像修正。包括220个中的所有样品，每个衰减和声果分别用于评估所提出的组合算法的分类能力。结果表明，腐烂和声果的鉴定准确性分别为97.3％和100％。组合双尺度经验模式分解和流域分割方法的多光谱主成分图像可以用作检测早期腐烂橙子的有效工具，并且对于开发快速和低成本的多光谱成像系统也很有希望。

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