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Updated: Apr 22, 2026

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Improving wildlife track classification through human-in-the-loop method and explainable AI.

Tinao Petso1, Rodrigo S Jamisola2, Sky Alibhai3,4

  • 1Department of Mechanical, Energy and Industrial Engineering, Botswana International University of Science and Technology, Private Bag 16, Palapye, Botswana. tinaopetso@gmail.com.

Scientific Reports
|April 20, 2026
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Summary
This summary is machine-generated.

Integrating expert tracker knowledge with artificial intelligence (AI) significantly improves wildlife species classification from track images. This AI model enhances accuracy and reduces training data needs, aiding conservation efforts.

Keywords:
Artificial intelligence modelsExplainable AIHuman-in-the-loopSpecies classificationWildlife tracks

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Area of Science:

  • Ecology and Conservation Biology
  • Computer Science and Artificial Intelligence
  • Wildlife Biology

Background:

  • Accurate wildlife species identification is crucial for effective conservation and ecological monitoring.
  • Traditional methods of species identification from tracks can be time-consuming and require extensive expertise.
  • Artificial intelligence offers a potential solution for automating and enhancing the accuracy of wildlife track classification.

Purpose of the Study:

  • To integrate expert tracker knowledge with artificial intelligence (AI) for wildlife species classification using track images.
  • To investigate the application of explainable AI (XAI) in conjunction with expert human evaluation.
  • To assess the performance improvement of AI models trained with expert-guided data and evaluated with human-in-the-loop feedback.

Main Methods:

  • Collected and curated a dataset of wildlife track images, including species like rhinoceros and wildebeest.
  • Trained and optimized AI models using various hyperparameter settings and training data quantities.
  • Incorporated expert tracker evaluations on image quality and AI model performance, utilizing both raw and heatmap visualizations for explainability.
  • Quantitatively evaluated AI model performance using mean average precision (mAP@50-95).

Main Results:

  • The integrated AI approach significantly increased mean average precision@50-95 by 10.42% compared to non-expert baseline.
  • Utilizing input from highly skilled expert trackers reduced the required number of training images by 25%.
  • Visual heatmaps generated by the AI model effectively supported explainable AI, aiding expert tracker evaluation.

Conclusions:

  • Combining expert tracker expertise with AI models substantially enhances wildlife species classification accuracy from track images.
  • Expert input is vital for optimizing AI model training, reducing data requirements and improving performance.
  • Explainable AI techniques, such as visual heatmaps, facilitate better understanding and validation of AI-driven wildlife identification.