Tea disease identification based on ECA attention mechanism ResNet50 network.

Abstract

Addressing the challenge of identifying tea plant diseases against the complex background of tea gardens, this study proposes the ECA-ResNet50 model. By optimizing the ResNet50 architecture, adopting a multi-layer small convolution kernel strategy to enhance feature extraction capabilities, and introducing the ECA attention mechanism to focus on key features, the model achieves a 93.06% accuracy rate in tea disease identification, representing a 3.18% improvement over the original model, demonstrating industry-leading performance advantages. This model not only accurately identifies tea diseases in gardens but also possesses excellent generalization capabilities, performing outstandingly on datasets of other plant categories. These results indicate that ECA-ResNet50 can effectively mitigate the interference of complex backgrounds and precisely recognize tea disease targets.

Code and data availability

The paper's tea disease image dataset (885 images, seven disease types plus healthy leaves) is a public Kaggle dataset explicitly cited by the authors with URL. No author analysis code or trained model checkpoints are stated as publicly available; the data availability statement only offers raw data on request.