Hyperspectral imaging (HSI) has become a key technology for non-invasive quality evaluation in various fields, offering detailed insights through spatial and spectral data. Despite its efficacy, the complexity and high cost of HSI systems have hindered their widespread adoption. This study addressed these challenges by exploring deep learning-based hyperspectral image reconstruction from RGB (Red, Green, Blue) images, particularly for agricultural products. Specifically, different hyperspectral reconstruction algorithms, such as Hyperspectral Convolutional Neural Network - Dense (HSCNN-D), High-Resolution Network (HRNET), and Multi-Scale Transformer Plus Plus (MST++), were compared to assess the dry matter content of sweet potatoes. Among the tested reconstruction methods, HRNET demonstrated superior performance, achieving the lowest mean relative absolute error (MRAE) of 0.07, root mean square error (RMSE) of 0.03, and the highest peak signal-to-noise ratio (PSNR) of 32.28 decibels (dB). Some key features were selected using the genetic algorithm (GA), and their importance was interpreted using explainable artificial intelligence (XAI). Partial least squares regression (PLSR) models were developed using the RGB, reconstructed, and ground truth (GT) data. The visual and spectra quality of these reconstructed methods was compared with GT data, and predicted maps were generated. The results revealed the prospect of deep learning-based hyperspectral image reconstruction as a cost-effective and efficient quality assessment tool for agricultural and biological applications.

翻译：高光谱成像（HSI）已成为多个领域进行非侵入式质量评估的关键技术，通过空间与光谱数据提供详细洞察。尽管其效能显著，但HSI系统的复杂性和高成本阻碍了其广泛应用。本研究通过探索基于深度学习的RGB（红、绿、蓝）图像高光谱重建技术，特别是针对农产品，以应对这些挑战。具体而言，本研究比较了不同的高光谱重建算法，如高光谱卷积神经网络-密集（HSCNN-D）、高分辨率网络（HRNET）和多尺度Transformer Plus Plus（MST++），以评估甘薯的干物质含量。在测试的重建方法中，HRNET表现出最优性能，取得了最低的平均相对绝对误差（MRAE）0.07、均方根误差（RMSE）0.03，以及最高的峰值信噪比（PSNR）32.28分贝（dB）。研究利用遗传算法（GA）筛选了关键特征，并借助可解释人工智能（XAI）阐释了其重要性。基于RGB数据、重建数据及真实值（GT）数据，构建了偏最小二乘回归（PLSR）模型。将重建方法的视觉与光谱质量与GT数据进行比较，并生成了预测图。结果表明，基于深度学习的高光谱图像重建技术有望成为农业与生物应用中一种经济高效的质量评估工具。