Tea grading, blending, and matching based on computer vision and deep learning

These results highlight the importance of attention mechanisms in improving the analysis of images with intricate textures. The integration of deep learning and attention modules enhanced the accuracy and efficiency of tea quality evaluation processes effectively. This study underscores the transformative potential of intelligent classification and analysis methods in modernizing tea production, ensuring higher standards of consistency and quality. © 2024 Society of Chemical Industry.