Quantifying and Mitigating Domain Shift in Peach Leaf Damage Classification: Attention Mechanisms and Fine-Tuning Strategies
Researchers used deep learning models enhanced with Convolutional Block Attention Module (CBAM) to classify peach leaf damage from images. They found that CBAM significantly improved performance, especially for rare damage types like mite presence, and that a specific fine-tuning strategy—partial fine-tuning—was the most effective method for adapting models to new field conditions (domain shift).