Chrysanthemum classification via color space fusion transformer

Abstract

Chrysanthemum is a traditional Chinese medicinal herb that contains significant medicinal and economic value. However, the medicinal and economic value of chrysanthemum differs depending on its regions and types. Therefore, it is valuable to classify chrysanthemum accurately. Traditional classification methods are costly, time-consuming, and mainly rely on manual processes, chemical testing, or genetic analysis. In light of these challenges, this paper proposes a Chrysanthemum Classification via Color Space Fusion Transformer, which is both cost-effective and capable of real-time processing. First, the chrysanthemum images in the RGB color space are converted to the LAB color space. Second, a multi-path network is designed to independently extract color space features from both the RGB and LAB color spaces, followed by their integration through an inter-path fusion module. Finally, the Transformer module further analyzes the semantic characteristics of these extracted color space features. Experimental results indicate that the proposed method achieves superior accuracy and stability compared to existing classification methods, with a classification accuracy of 96.16%. This method provides an efficient and practical solution for chrysanthemum origin traceability.

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