Abstract The presence of surface defects is one of the most influential factors in the quality and price of fresh fruit because consumers usually associate quality with a good appearance and without skin defects. Therefore, one of the main purposes of automatic detection of fruit quality is to differentiate between defective ones from sound fruits. However, the detection of defective fruits has always been a challenging task, especially the simultaneous detection of multiple types of defects. This work focuses on the development of multispectral image classification algorithm for detecting the common defects on mandarins based on the visible-near infrared (Vis-NIR) hyperspectral imaging technique. ‘Nanfeng’ mandarins with sound peel and four types of defects (i.e., anthracnose, scarring, decay and thrips scarring) were studied. Principal component analysis (PCA) was used to reduce hyperspectral data dimensionality with the goal of selecting several wavelengths that could be potentially used in an in-line multispectral imaging system. Two characteristic wavelength images at 680 nm and 715 nm in the visible spectral region were selected, and then the second principal component image (PC-2) and ratio image (Q680/715) based on these two characteristic wavelengths were used for defect detection and stem-end identification, respectively. Finally, the detection algorithm of defects was developed based on PC-2 image and ratio image (Q680/715) coupled with a simple thresholding method. For the investigated 356 independent test samples, classification accuracy of 96.63% indicated that the proposed multispectral image algorithm was effective for distinguishing between sound and defective ‘Nanfeng’ mandarins. Only two wavelength images were used in the algorithm, which was very helpful to develop a fast multispectral imaging system for on-line grading of mandarins.

中文翻译：

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使用可见光和近红外高光谱成像检测蜜桔常见缺陷

摘要 表面缺陷的存在是影响新鲜水果质量和价格的最重要因素之一，因为消费者通常将质量与良好的外观和无果皮联系在一起。因此，水果质量自动检测的主要目的之一是区分缺陷水果和完好水果。然而，缺陷水果的检测一直是一项具有挑战性的任务，尤其是同时检测多种类型的缺陷。这项工作的重点是基于可见-近红外（Vis-NIR）高光谱成像技术开发用于检测普通话常见缺陷的多光谱图像分类算法。对具有良好果皮和四种缺陷（即炭疽病、疤痕、腐烂和蓟马疤痕）的'南丰'蜜桔进行了研究。主成分分析 (PCA) 用于降低高光谱数据维数，目的是选择可能用于在线多光谱成像系统的几个波长。选取可见光谱区680 nm和715 nm的两张特征波长图像，然后基于这两个特征波长的第二主成分图像（PC-2）和比值图像（Q680/715）用于缺陷检测和分别进行茎端鉴定。最后，基于PC-2图像和比率图像（Q680/715）结合简单的阈值方法开发了缺陷检测算法。对于调查的 356 个独立测试样本，分类准确率为 96。63% 的人表示，所提出的多光谱图像算法可有效区分声音和有缺陷的“南丰”蜜桔。该算法仅使用了两幅波长图像，这对于开发用于在线分级的蜜桔快速多光谱成像系统非常有帮助。