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Mushroom image classification and recognition based on improved ConvNeXt V2.

Shulong Zhang1,2,3, Kexin Zhao1,2,3, Yukang Huo1,2,3

  • 1National Innovation Center for Digital Fishery, Beijing, People's Republic of China.

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|March 17, 2025
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Summary
This summary is machine-generated.

This study introduces an improved ConvNeXt V2 model for accurate wild mushroom identification using images. The enhanced model significantly boosts classification accuracy, aiding in preventing mushroom poisoning incidents.

Keywords:
convNeXt v2deep learningmushroom image

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Area of Science:

  • Computer Vision
  • Mycology
  • Artificial Intelligence

Background:

  • Accurate wild mushroom identification is crucial for preventing poisoning, but natural scene complexity and morphological similarities pose challenges.
  • Existing methods struggle with the nuances of wild mushroom classification in diverse environments.

Purpose of the Study:

  • To develop an improved ConvNeXt V2 network model for robust classification and recognition of wild mushroom species in complex scenes.
  • To enhance the model's feature extraction and capture capabilities for improved accuracy in mushroom identification.

Main Methods:

  • A dataset of 10,986 mushroom images across 18 categories was constructed and augmented using techniques like image flipping, noise addition, and mosaic.
  • A cross-modular approach was employed for multi-dimensional feature extraction and fusion, optimizing the ConvNeXt V2 architecture.
  • The model was further refined with one-hot encoding and spatial pyramid pooling for enhanced performance.

Main Results:

  • The improved ConvNeXt V2 model achieved high performance metrics: 96.7% accuracy, 96.84% precision, 96.83% recall, and 96.84% F1-Score.
  • Ablation experiments confirmed the effectiveness of the proposed improvements, demonstrating superior performance over comparative models like ResNet and Swin Transformer.
  • The model significantly enhanced efficiency and accuracy in classifying and recognizing mushroom images.

Conclusions:

  • The developed improved ConvNeXt V2 model offers a highly effective solution for wild mushroom image classification, outperforming existing state-of-the-art methods.
  • This technology provides vital technical support for identifying edible versus non-edible mushrooms, thereby reducing poisoning incidents and ensuring food safety.