  Hyperspectral imaging (HSI) has recently emerged as a promising tool for many
agricultural applications; however, the technology cannot be directly used in a
real-time system due to the extensive time needed to process large volumes of
data. Consequently, the development of a simple, compact, and cost-effective
imaging system is not possible with the current HSI systems. Therefore, the
overall goal of this study was to reconstruct hyperspectral images from RGB
images through deep learning for agricultural applications. Specifically, this
study used Hyperspectral Convolutional Neural Network - Dense (HSCNN-D) to
reconstruct hyperspectral images from RGB images for predicting soluble solid
content (SSC) in sweet potatoes. The algorithm accurately reconstructed the
hyperspectral images from RGB images, with the resulting spectra closely
matching the ground-truth. The partial least squares regression (PLSR) model
based on reconstructed spectra outperformed the model using the full spectral
range, demonstrating its potential for SSC prediction in sweet potatoes. These
findings highlight the potential of deep learning-based hyperspectral image
reconstruction as a low-cost, efficient tool for various agricultural uses.
