Non-Destructive Authentication of Rice Varieties Using Hyperspectral Imaging and Machine Learning

Document Type : Original Research

Authors

1 Information Technology and Intelligent Systems Group, Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran.

2 Biomedical Engineering Group, Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran.

10.22103/bbr.2026.26384.1142

Abstract

This study develops a non-destructive method for authenticating four commercial rice varieties: Sargol, Domsiyah, AliKazemi, and Aria-Paria, by integrating hyperspectral imaging (HSI) with machine learning. Hyperspectral data in the visible–near infrared range (400–950 nm) were acquired from 4,305 individual rice grains. Effective preprocessing mitigated initial anisotropic spatial sampling by resizing images to achieve isotropic resolution, preventing grain loss during segmentation. Two analytical strategies were investigated: one relying on handcrafted features and another directly exploiting reduced spectral profiles as sequential data. Comparative evaluation showed that models trained on sequential spectral information consistently outperformed feature-based methods. Among the evaluated classifiers, a Support Vector Machine (SVM) achieved the highest classification accuracy of 92.62%, exceeding both classical machine learning models and deep learning approaches, including Long Short-Term Memory (LSTM) networks, one-dimensional Convolutional Neural Networks (1D-CNNs), and a hybrid CNN-LSTM architecture. The proposed HSI–SVM framework demonstrates strong potential for accurate rice variety authentication and offers practical applicability in quality control and supply chain monitoring.

Keywords

References

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Biomechanism and Bioenergy Research
Volume 5, Issue 1
March 2026
Pages 1-19

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History

  • Receive Date: 25 November 2025
  • Revise Date: 08 January 2026
  • Accept Date: 26 February 2026

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