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An Intelligent QR Code–Enabled Framework for Digital Identification and Management of Zoological Specimens

Author

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  • V. Queen Jemila
  • P. Vijaya

Abstract

In zoological labs and museums, precise biological specimen identification, preservation, and retrieval are essential criteria. Conventional specimen marking techniques are frequently vulnerable to human mistake, data loss, and physical deterioration. This study offers an interdisciplinary framework that uses artificial intelligence (AI) and QR code technology to combine computer science methods with zoological taxonomy in order to address these issues. The suggested system uses machine-readable QR codes and AI-assisted data processing to enable the digital identification, validation, storage, and retrieval of zoological specimen information. Each specimen is assigned a unique QR code that links to a validated digital record containing taxonomic, morphological, and collection-related information. The framework increases long-term specimen management, decreases manual errors, and improves accessibility. The method is especially well-suited for museums, biodiversity documentation centers, and college zoology labs.

Suggested Citation

  • V. Queen Jemila & P. Vijaya, 2026. "An Intelligent QR Code–Enabled Framework for Digital Identification and Management of Zoological Specimens," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 13(2), pages 28-31, April.
    • Handle: RePEc:etm:ijsrst:v13:y2026:i2:id:1417
    DOI: 10.32628/IJSRST2613150
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