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A deep learning framework for bone fragment classification in owl pellets using YOLOv12.

Nik Fadzly1,2,3, Lay Wai Kean4, Siti Nuramaliati Prijono5

  • 1School of Biological Sciences, Universiti Sains Malaysia, USM, Pulau Pinang, 11800, Malaysia. nfadzly@usm.my.

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Summary

This study introduces an AI tool for automatically identifying rodent bones in owl pellets, improving small mammal population monitoring for conservation and pest management. The automated system enhances accuracy and efficiency in ecological surveys.

Keywords:
Artificial intelligenceBone classificationMachine learningObject detectionOwl pellets

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Area of Science:

  • Ecology
  • Artificial Intelligence
  • Conservation Biology

Background:

  • Non-invasive monitoring of small mammal populations is crucial for biodiversity conservation and pest management in agroecosystems.
  • Barn owl pellet analysis is a traditional method for estimating prey abundance, but it is labor-intensive and requires expert knowledge for bone classification.

Purpose of the Study:

  • To develop and validate a deep learning framework for automated detection and classification of rodent bone fragments in owl pellets.
  • To create an AI-assisted workflow for scalable and efficient rodent abundance estimation.

Main Methods:

  • Utilized the YOLOv12 object detection architecture for automated bone fragment identification.
  • Trained and validated the model on a dataset of 978 annotated images of rodent bones (skull, femur, mandible, pubis).
  • Developed a Python script for inferring rodent abundance from bone counts.

Main Results:

  • Achieved high detection performance with precision=0.90, recall=0.90, mAP@0.5=0.984, and F1-score=0.97.
  • Demonstrated strong model generalization across samples from Malaysia and Indonesia.
  • The AI workflow significantly reduces human error and increases processing throughput.

Conclusions:

  • The AI-powered approach enhances the accuracy and efficiency of ecological inference from owl pellet studies.
  • This method enables scalable rodent monitoring, supporting timely biodiversity assessments and pest surveillance.
  • The framework offers a valuable tool for researchers and land managers in agroecosystems and beyond.