Deep learning-based hyperspectral image reconstruction for quality assessment of agro-product

TL;DR

This paper presents a deep learning method to reconstruct hyperspectral images from RGB images, enabling accurate prediction of soluble solid content in sweet potatoes, thus offering a cost-effective tool for agricultural quality assessment.

Contribution

The study introduces a deep learning-based hyperspectral reconstruction method from RGB images specifically for agricultural applications, improving efficiency and cost-effectiveness.

Findings

Reconstructed hyperspectral images closely match ground-truth spectra.

PLSR model with reconstructed spectra outperforms full spectral range model.

Demonstrates potential for low-cost, real-time agricultural quality assessment.

Abstract

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.