Comparative analysis of hyperspectral Image reconstruction using deep learning for agricultural and biological applications

Ahmed, Md Toukir; Villordon, Arthur; Kamruzzaman, Mohammed

doi:10.1016/j.rineng.2024.102623

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Hyperspectral imaging and explainable deep-learning for non-destructive quality prediction of sweetpotato

Ahmed,

Villordon,

Kamruzzaman

2025

Postharvest Biology and Technology

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Hyperspectral imaging and explainable deep-learning for non-destructive quality prediction of sweetpotato

Ahmed,

Villordon,

Kamruzzaman

2025

Postharvest Biology and Technology

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

Ahmed,

Monjur,

Khaliduzzaman

et al. 2024

Preprint

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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.

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Adaptive Feature Extraction and Attention-Based Segmentation Network for Remote Sensing Imagery

Azmat,

Azam,

Bhatti

et al. 2024

Preprint

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Comparative analysis of hyperspectral Image reconstruction using deep learning for agricultural and biological applications

Cited by 3 publications

References 43 publications

Hyperspectral imaging and explainable deep-learning for non-destructive quality prediction of sweetpotato

Hyperspectral imaging and explainable deep-learning for non-destructive quality prediction of sweetpotato

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

Adaptive Feature Extraction and Attention-Based Segmentation Network for Remote Sensing Imagery

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