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Chrysanthemum classification method integrating deep visual features from both the front and back sides.

Yifan Chen¹, Xichen Yang¹, Hui Yan^2,3

¹School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing, Jiangsu, China.

Frontiers in Plant Science

|February 5, 2025

Summary

This summary is machine-generated.

A new method uses deep learning to classify Chrysanthemum (Chrysanthemum morifolium Ramat) by fusing front and back image features. This swift, non-invasive technique achieves 93.8% accuracy, improving herbal crop identification.

Keywords:

Chrysanthemum classification deep learning feature fusion two-stream network visual information

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Area of Science:

Agricultural Science
Computer Vision
Biotechnology

Background:

Chrysanthemum morifolium Ramat is a valuable Chinese herbal crop with significant medicinal applications.
Accurate classification and origin identification are crucial for producers, consumers, and market regulation.
Current identification methods are subjective, time-consuming, and require expensive equipment.

Purpose of the Study:

To develop a novel, swift, non-invasive, and non-contact method for Chrysanthemum classification and origin identification.
To improve the accuracy and efficiency of Chrysanthemum identification compared to existing methods.

Main Methods:

Image preprocessing to remove background noise.
A two-stream deep learning network utilizing front and back Chrysanthemum images.
Fusion of deep visual features using single-stream and cross-stream residual connections.

Main Results:

The proposed method achieved a classification accuracy of 93.8%.
The approach demonstrated superior stability and outperformed existing identification methods.
The method provides an effective and dependable solution for Chrysanthemum identification.

Conclusions:

The developed deep learning method offers an efficient and accurate solution for Chrysanthemum classification and origin identification.
This technique has practical benefits for quality assurance in agricultural production and markets.
The study provides a valuable tool for regulatory processes concerning herbal crops.