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Combination Ensemble and Explainable Deep Learning Framework for High-Accuracy Classification of Wild Edible

Aras Fahrettin Korkmaz1, Fatih Ekinci2, Eda Kumru3

  • 1Faculty of Health Sciences Nutrition, Dietetics Department, Şirinevler Campus, İstanbul Kültür University, 34191 Istanbul, Türkiye.

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|December 30, 2025
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Summary
This summary is machine-generated.

Accurate wild edible macrofungi identification is crucial. A novel ensemble model combining CNNs and explainable AI achieved 97.36% accuracy, improving food safety and biodiversity conservation.

Keywords:
deep learningedible mushroomensemble modelsexplainable AIspecies classification

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Area of Science:

  • Mycology
  • Computer Science
  • Artificial Intelligence

Background:

  • Accurate identification of wild edible macrofungi is vital for biodiversity, food safety, and ecological sustainability.
  • Morphological similarities between edible and toxic species pose significant identification challenges.

Purpose of the Study:

  • To develop and evaluate advanced machine learning models for precise wild edible macrofungi identification.
  • To enhance model interpretability using explainable AI (XAI) techniques.

Main Methods:

  • Analysis of a curated dataset of 24 wild edible macrofungi species.
  • Benchmarking six state-of-the-art convolutional neural networks (CNNs) and four ensemble configurations.
  • Integration of EfficientNetB0, ResNet50, and RegNetY in a hierarchical voting Combination Model.
  • Application of XAI methods (Grad-CAM, Eigen-CAM, LIME) for model interpretability.

Main Results:

  • The Combination Model achieved the highest accuracy (97.36%), AUC (0.9996), and MCC (0.9725).
  • EfficientNetB0 performed best among individual CNNs (95.55% accuracy).
  • XAI methods successfully highlighted biologically relevant regions, enhancing model transparency.

Conclusions:

  • Engineered ensemble learning combined with XAI provides a robust and scalable solution for fine-grained fungal classification.
  • This approach significantly advances mycological research and offers potential for broader applications in ecological monitoring and species recognition.