Chrysanthemum image quality assessment via multi-scale feature fusion and meta-learning

Key Points

• Image quality assessment improved using multi-scale features and meta-learning approaches for chrysanthemum images.
• The analysis revealed that incorporating feature fusion techniques led to superior assessment outcomes, particularly in detailed evaluations.
• Employing a meta-learning framework significantly enhances the understanding of image quality variations across different scales in botanicals.
• This study highlights the importance of advanced image processing methods, suggesting further exploration in wider agricultural applications.