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BarkVisionAI: Novel dataset for rapid tree species identification.

Ashwini Chhatre1, Nitesh Saini2, Abhijeet Kumar Parmar2

  • 1Bharti Institute of Public Policy, Indian School of Business, Hyderabad, India. ashwini_chhatre@isb.edu.

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|February 3, 2026
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Summary

This study introduces BarkVisionAI, a large dataset of tree bark images for 13 Indian species. This resource aids automated tree identification and ecological research, improving forest management and biodiversity conservation efforts.

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Area of Science:

  • Botany
  • Computer Science
  • Ecology

Background:

  • Accurate tree species identification is vital for forest management and conservation.
  • Ground-level bark images offer rich data but existing datasets lack diversity.
  • Limitations in current tree bark datasets hinder real-world applications.

Purpose of the Study:

  • To introduce BarkVisionAI, a comprehensive dataset of tree bark images.
  • To address the limitations of existing datasets in terms of species diversity and temporal attributes.
  • To facilitate research in automated tree species identification and ecological monitoring.

Main Methods:

  • Collected 156,001 tree bark images across 13 species from diverse Indian forest types.
  • Labeled each image with location, species, device attributes, and timestamp.
  • Conducted benchmarking experiments using standard image classification models.

Main Results:

  • BarkVisionAI comprises 156,001 labeled images for 13 tree species.
  • The dataset includes detailed metadata, enabling robust analysis.
  • Benchmarking demonstrated the dataset's effectiveness for machine learning models.

Conclusions:

  • BarkVisionAI is a valuable resource for automated tree species identification.
  • The dataset supports ecological research and environmental monitoring.
  • It facilitates the development of reliable, real-world applications for forestry.