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A Progressive Feature Learning Network for Cordyceps sinensis Image Recognition.

Shangdong Liu¹, Wenxiang Wu¹, Haijun Chen²

¹School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Sensors (Basel, Switzerland)

|November 27, 2025

Summary

This summary is machine-generated.

This study introduces a new AI method, the Progressive Feature Learning Network (PFL-Net), for accurate Cordyceps sinensis (C. sinensis) subspecies recognition. PFL-Net achieves high accuracy, overcoming challenges posed by similar subspecies morphology.

Keywords:

Cordyceps sinensis feature mining image recognition progressive learning

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Area of Science:

Botany
Computer Science
Machine Learning

Background:

Cordyceps sinensis (C. sinensis) is a valuable herbal medicine.
Automated recognition of C. sinensis subspecies is difficult due to high morphological similarity and limited phenotypic variation.

Purpose of the Study:

To develop a novel approach for automated, fine-grained recognition of C. sinensis subspecies.
To improve the accuracy and integrity of C. sinensis recognition by mining multiple biological features.

Main Methods:

Proposed the Progressive Feature Learning Network (PFL-Net) incorporating Spatial-aware Semantic Refinement Module (SSRM) and Multi-scale Collaborative Perception Module (MCPM).
Introduced a Channel Decouple (CD) loss to enhance feature diversity.
Constructed a C. sinensis dataset (CSD) for biometric recognition.

Main Results:

The PFL-Net achieved a top-1 accuracy of 94.43% on the CSD dataset.
The proposed method surpassed all existing approaches on the CSD and three benchmark datasets.
SSRM and MCPM effectively captured multi-scale discriminative features and enhanced recognition integrity.

Conclusions:

The PFL-Net demonstrates superior performance in fine-grained C. sinensis subspecies recognition.
The novel modules and CD loss contribute to enhanced feature learning and diversity.
This work provides a robust solution for C. sinensis biometric recognition and lays the foundation for future research.