Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
J Food Sci. 2023 Dec;88(12):5149-5163. doi: 10.1111/1750-3841.16801. Epub 2023 Oct 25.
Recent advances in hyperspectral imaging (HSI) have demonstrated its ability to detect defects in fruit that may not be visible in RGB images. HSIs can be considered 3D images containing two spatial dimensions and one spectral dimension. Therefore, the first question that arises is how to process this type of information, either using 2D or 3D models. In this study, HSI in the 550-900 nm spectral range was used to detect bruising in oranges. Sixty samples of Thompson oranges were subjected to a mechanical bruising process, and HSIs were taken at different time intervals: before bruising, and 8 and 16 h after bruising. The samples were then classified using two convolutional neural network (CNN) models, a shallow 7-layer network (CNN-7) and a deep 18-layer network (CNN-18). In addition, two different input processing approaches are used: using 2D information from each band, and using the full 3D data from each HSI. The 3D models were the most accurate, with 94% correct classification for 3D-CNN-18, compared to 90% for 3D-CNN-7, and less than 83% for the 2D models. Our study suggests that 3D HSI may be a more effective technique for detecting fruit bruising, allowing the development of a fast, accurate, and nondestructive method for fruit sorting. PRACTICAL APPLICATION: Orange bruises can reduce the market value of food, which is why the food processing industry needs to carry out quality inspections. An effective way to perform this inspection is by using hyperspectral images that can be processed with 2D or 3D models, either with deep or shallow neural networks. The results of the comparison performed in this work can be useful for the development of more accurate and efficient bruise detection methods for fruit inspection.
近年来，高光谱成像（HSI）的发展已经证明了它能够检测到在 RGB 图像中不可见的水果缺陷。HSI 可以被视为包含两个空间维度和一个光谱维度的 3D 图像。因此，首先出现的问题是如何处理这种类型的信息，无论是使用 2D 还是 3D 模型。在这项研究中，使用 550-900nm 光谱范围内的 HSI 来检测橙子的瘀伤。将 60 个汤普森橙子样本进行机械瘀伤处理，并在不同时间间隔拍摄 HSI：瘀伤前以及瘀伤后 8 和 16 小时。然后使用两个卷积神经网络（CNN）模型对样本进行分类，一个是浅层 7 层网络（CNN-7），另一个是深层 18 层网络（CNN-18）。此外，还使用了两种不同的输入处理方法：使用每个波段的 2D 信息，以及使用每个 HSI 的完整 3D 数据。3D 模型最准确，3D-CNN-18 的正确分类率为 94%，而 3D-CNN-7 为 90%，2D 模型则低于 83%。我们的研究表明，3D HSI 可能是一种更有效的检测水果瘀伤的技术，它可以为水果分拣开发出一种快速、准确和无损的方法。 实际应用：橙子瘀伤会降低食品的市场价值，这就是食品加工行业需要进行质量检查的原因。进行这种检查的一种有效方法是使用高光谱图像，可以使用 2D 或 3D 模型进行处理，无论是使用深度还是浅层神经网络。这项工作中进行的比较结果对于开发更准确和高效的水果检测瘀伤检测方法可能会很有用。
