# Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications

arXiv: https://arxiv.org/abs/2405.13331 · HTML: https://mlsanity.com/paper/2405.13331/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction](https://mlsanity.com/paper/2204.07908/) (2022)
- [NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image](https://mlsanity.com/paper/2005.03412/) (2020)
- [Hierarchical Regression Network for Spectral Reconstruction from RGB Images](https://mlsanity.com/paper/2005.04703/) (2020)
- [A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging](https://mlsanity.com/paper/2106.15944/) (2021)
- [Remote sensing and AI for building climate adaptation applications](https://mlsanity.com/paper/2107.02693/) (2021)
- [SPINEX: Similarity-based Predictions and Explainable Neighbors Exploration for Regression and Classification Tasks in Machine Learning](https://mlsanity.com/paper/2306.01029/) (2023)
- [Deep learning-based hyperspectral image reconstruction for quality assessment of agro-product](https://mlsanity.com/paper/2405.12313/) (2024)
- [MPVNN: Mutated Pathway Visible Neural Network Architecture for Interpretable Prediction of Cancer-specific Survival Risk](https://mlsanity.com/paper/2202.00882/) (2022)
- [PAMI: partition input and aggregate outputs for model interpretation](https://mlsanity.com/paper/2302.03318/) (2023)

## Built on by (most cited first, 1 of 1)

- [Uncertainty Quantification in HSI Reconstruction using Physics-Aware Diffusion Priors and Optics-Encoded Measurements](https://mlsanity.com/paper/2511.18473/) (2025)

## Abstract (arXiv)

Hyperspectral imaging (HSI) has become a key technology for non-invasive quality evaluation in various fields, offering detailed insights through spatial and spectral data. Despite its efficacy, the complexity and high cost of HSI systems have hindered their widespread adoption. This study addressed these challenges by exploring deep learning-based hyperspectral image reconstruction from RGB (Red, Green, Blue) images, particularly for agricultural products. Specifically, different hyperspectral reconstruction algorithms, such as Hyperspectral Convolutional Neural Network - Dense (HSCNN-D), High-Resolution Network (HRNET), and Multi-Scale Transformer Plus Plus (MST++), were compared to assess the dry matter content of sweet potatoes. Among the tested reconstruction methods, HRNET demonstrated superior performance, achieving the lowest mean relative absolute error (MRAE) of 0.07, root mean square error (RMSE) of 0.03, and the highest peak signal-to-noise ratio (PSNR) of 32.28 decibels (dB). Some key features were selected using the genetic algorithm (GA), and their importance was interpreted using explainable artificial intelligence (XAI). Partial least squares regression (PLSR) models were developed using the RGB, reconstructed, and ground truth (GT) data. The visual and spectra quality of these reconstructed methods was compared with GT data, and predicted maps were generated. The results revealed the prospect of deep learning-based hyperspectral image reconstruction as a cost-effective and efficient quality assessment tool for agricultural and biological applications.