Multi-scale feature fusion for chrysanthemum classification using dual-view

Key Points

• Chrysanthemum classification accuracy improves with dual-view feature fusion, showing a significant increase in identification rates.
• Key metric reveals a marked lift in performance compared to single-view methods, enhancing classification outcomes.
• Analysis of multiple scales and features in the images contributes to more robust classification capabilities.
• The findings suggest potential for refined techniques in plant identification, highlighting the need for further exploration in diverse settings.