Deep learning-based hyperspectral image reconstruction for quality assessment of agro-product
This addresses the need for cost-effective, real-time quality assessment tools in agriculture, though it is incremental as it applies an existing deep learning approach to a specific agricultural task.
The study tackled the problem of slow hyperspectral imaging for real-time agricultural use by reconstructing hyperspectral images from RGB images using a deep learning method, achieving accurate spectra that improved soluble solid content prediction in sweet potatoes compared to using full spectral data.
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.