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…

• UD
  U.S. Department of Agriculture

  Award: AM21SCMPMS1010
• NI
  National Institute of Food and Agriculture

  Award: AM21SCMPMS1010
• AM
  Agricultural Marketing Service

  Award: AM21SCMPMS1010