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Innovative infrastructure to access Brazilian fungal diversity using deep learning.

Thiago Chaves1, Joicymara Santos Xavier2, Alfeu Gonçalves Dos Santos1

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Summary
This summary is machine-generated.

This study introduces a new database of Brazilian macrofungi to train convolutional neural networks (CNNs) for automated species identification, enabling a mobile app for public use and fungal conservation efforts.

Keywords:
CNNComputer visionDeep learningFungiImage classification

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Area of Science:

  • Mycology
  • Computer Science
  • Biodiversity Informatics

Background:

  • Accurate identification of macrofungal species is crucial for ecological studies and conservation.
  • Existing methods for macrofungal identification can be time-consuming and require expert knowledge.
  • There is a need for accessible tools to aid in the identification and documentation of Brazilian fungi.

Purpose of the Study:

  • To develop a comprehensive, expert-curated database of Brazilian macrofungi with extensive photographic data.
  • To train and validate convolutional neural networks (CNNs) for automated macrofungal species identification.
  • To create a user-friendly mobile application for image-based macrofungal identification, promoting public engagement and data collection.

Main Methods:

  • Compilation of a meticulously structured database of macrofungi from Brazil, including over 13,894 photographs of 505 species.
  • Training and validation of convolutional neural networks (CNNs) using the curated image database for autonomous species recognition.
  • Development of a mobile application with an advanced user interface for image acquisition and AI-driven identification suggestions.

Main Results:

  • Successful creation of a large-scale, expert-verified database of Brazilian macrofungi.
  • Demonstrated capability of trained CNNs to autonomously identify macrofungal species from images.
  • Development of a functional mobile application prototype for public use.

Conclusions:

  • The developed database and CNN models provide a powerful tool for macrofungal identification in Brazil.
  • The mobile application democratizes access to knowledge about Brazilian fungi, fostering public engagement and citizen science.
  • This technology supports enhanced biodiversity monitoring and conservation efforts for Brazilian macrofungi.