Abstract Rapid quality assessment of nuts is important to increase the shelf life and minimise the nut loss due to rancidity. Existing methods for nut quality estimation are usually slow and destructive. In this study, a quick and non-destructive method using hyperspectral imaging (HSI) coupled with deep learning classification was applied for the quality estimation of unblanched kernels in Canarium indicum categorized by peroxide values (PV). A set of 2300 sub-images of 289 C. indicum samples were used to train a convolutional neural network (CNN) to estimate quality levels. Series of ablation experiments showed that the highest overall accuracy of PV estimation on the test set reached 93.48%, with 95.59%, 90.00%, and 95.83% for good, medium, and poor quality nuts, respectively. The results indicate that deep learning classification of hyperspectral imagery offers a great potential for accurate, real-time, and non-destructive quality estimation of nuts in practical applications.

中文翻译：

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使用高光谱图像的深度学习分类对坚果的质量估计

摘要 坚果的快速质量评估对于延长保质期和减少因酸败造成的坚果损失非常重要。现有的坚果质量评估方法通常缓慢且具有破坏性。在这项研究中，使用高光谱成像 (HSI) 结合深度学习分类的快速无损方法被应用于按过氧化值 (PV) 分类的 Canarium indicum 未漂白谷粒的质量估计。使用 289 个 C. indicum 样本的 2300 个子图像来训练卷积神经网络 (CNN) 以估计质量水平。一系列的消融实验表明，测试集上 PV 估计的最高总体准确率达到了 93.48%，优质、中等和劣质坚果分别为 95.59%、90.00% 和 95.83%。