  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.
