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Related Experiment Video

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Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi

Sifa Ozsari1, Eda Kumru2, Fatih Ekinci3

  • 1Department of Computer Engineering, Faculty of Engineering, Ankara University, Ankara 06830, Türkiye.

Sensors (Basel, Switzerland)
|November 27, 2024
PubMed
Summary

This study classifies six macrofungi species using deep learning. The DenseNet121 model achieved 92% accuracy, showing promise for AI in biodiversity research and fungi conservation.

Keywords:
DenseNet121deep learningfungi identificationmachine learning modelsmacrofungi classification

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Area of Science:

  • Mycology
  • Computer Science
  • Artificial Intelligence

Background:

  • Accurate identification of macrofungi species is crucial for ecological research and conservation.
  • Traditional methods of fungi classification can be time-consuming and require specialized expertise.
  • Deep learning offers a potential solution for automated and efficient macrofungi identification.

Purpose of the Study:

  • To classify six distinct macrofungi species using advanced deep learning models.
  • To evaluate the performance of various machine learning and deep learning techniques for fungi image recognition.
  • To identify the most effective deep learning model for accurate macrofungi species classification.

Main Methods:

  • Utilized 5 machine learning techniques and 12 deep learning models, including DenseNet121, MobileNetV2, ConvNeXt, EfficientNet, and swin transformers.
  • Trained models on images of six macrofungi species: Amanita pantherina, Boletus edulis, Cantharellus cibarius, Lactarius deliciosus, Pleurotus ostreatus, and Tricholoma terreum.
  • Evaluated model performance based on accuracy and Area Under the Curve (AUC) scores.

Main Results:

  • The DenseNet121 model achieved the highest classification accuracy (92%) and AUC score (95%).
  • Transformer-based models, specifically the swin transformer, showed lower effectiveness in this classification task.
  • The study highlights the potential of deep learning for distinguishing between macrofungi species based on visual data.

Conclusions:

  • Deep learning, particularly the DenseNet121 architecture, is highly effective for macrofungi species classification.
  • Further improvements can be made by expanding datasets, integrating diverse data types (e.g., biochemical, genetic), and employing ensemble methods.
  • This research provides valuable insights for advancing biodiversity research and informing the ecological conservation of macrofungi.