Abstract Over the past decades, Red-Green-Blue (RGB) image analysis has gained increasing importance in industrial applications, since it has widely proved to be a suitable tool for food quality and process control. This article describes the development of a fast and objective method for the automated identification of defective hazelnut kernels based on multivariate analysis of RGB images. To this aim, an overall sample set of 2000 half-cut hazelnut kernels, previously assigned by industrial expert assessors as sound or defective (i.e. rotten or pest-affected), was collected and imaged using a digital camera. The colour-related information of the images was converted into one-dimensional signals, named colourgrams, which were firstly explored through the Principal Component Analysis and subsequently used to build classification models, based on both Partial Least Square-Discriminant Analysis (PLS-DA) and interval-PLS-DA (iPLS-DA) algorithms. A tree-structure hierarchical classification approach has been considered, i.e. the discrimination between sound and defective kernels as a first rule, and the discrimination between the two types of defect as a second rule. The best sound vs defective classification model was able to correctly recognize approximately the 97% of the test set defective samples, while the best rotten vs pest-affected model allowed classifying correctly more than 92% of the test set samples. Moreover, the image reconstruction performed using the selected colourgram features led to an exhaustive interpretation of the decision-making criteria adopted by the classification algorithms and further confirmed the reliability of the proposed method.

中文翻译：

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基于 RGB 图像分析和颜色图的缺陷榛子自动识别方法的开发

摘要 在过去的几十年里，红绿蓝 (RGB) 图像分析在工业应用中越来越重要，因为它已被广泛证明是一种适用于食品质量和过程控制的工具。本文介绍了基于 RGB 图像的多变量分析自动识别缺陷榛子仁的快速客观方法的开发。为此，收集了 2000 个半切榛子仁的整体样本集，使用数码相机收集并成像，之前由工业专家评估员指定为完好或有缺陷（即腐烂或受害虫影响）。图像的颜色相关信息被转换为一维信号，称为色图，首先通过主成分分析进行探索，随后用于构建分类模型，基于偏最小二乘判别分析 (PLS-DA) 和区间 PLS-DA (iPLS-DA) 算法。树结构分层分类方法已被考虑，即区分健全和有缺陷的内核作为第一规则，区分两种类型的缺陷作为第二规则。最佳声音 vs 缺陷分类模型能够正确识别大约 97% 的测试集缺陷样本，而最佳腐烂 vs 受害虫影响模型允许正确分类超过 92% 的测试集样本。此外，使用所选色图特征执行的图像重建导致对分类算法采用的决策标准的详尽解释，并进一步证实了所提出方法的可靠性。