A comprehensive review of deep learning-based hyperspectral image reconstruction for agri-food quality appraisal

Abstract

Hyperspectral imaging (HSI) has recently emerged as a promising tool for various agricultural applications. However, high equipment cost, instrumentation complexity, and data-intensive nature have limited its widespread adoption. To overcome these challenges, reconstructing hyperspectral data from simple, cost-effective color or RGB (red-green-blue) images using advanced deep learning algorithms offers a practically attractive solution for a wide range of applications in food quality control and assurance. Through advanced deep learning algorithms, it is possible to capture and reconstruct spectral information from simple, cost-effective RGB imaging to create a reliable, efficient, and scalable system with accuracy comparable to dedicated, expensive HSI systems. This review provides a comprehensive overview of recent advances in deep learning techniques for HSI reconstruction and highlights the transformative impact of deep learning-based hyperspectral image reconstruction on agricultural and food products and anticipates a future where these innovations will lead to more advanced and widespread applications in the agri-food industry.