SpemNet: A Cotton Disease and Pest Identification Method Based on Efficient Multi-Scale Attention and Stacking Patch Embedding

Simple Summary Cotton is a crucial economic crop, but it is often threatened by various pests and diseases during its growth, significantly impacting its yield and quality. Earlier image classification methods often suffer from low accuracy and struggle to perform effectively in complex real-world environments. This paper proposes a novel image classification network named SpemNet, specifically designed for cotton pest and disease recognition. By introducing the Efficient Multi-Scale Attention (EMA) module and the Stacking Patch Embedding (SPE) module, the network enhances the ability to learn local features and integrate multi-scale information, thereby significantly improving the accuracy and efficiency of cotton pest and disease recognition. Extensive experiments conducted on the publicly available CottonInsect and IP102 datasets, as well as a self-collected cotton leaf disease dataset, demonstrate that SpemNet exhibits significant advantages in key metrics such as precision, recall, and F1 score, confirming its effectiveness and superiority in the task of cotton pest and disease recognition.Abstract We propose a cotton pest and disease recognition method, SpemNet, based on efficient multi-scale attention and stacking patch embedding. By introducing the SPE module and the EMA module, we successfully solve the problems of local feature learning difficulty and insufficient multi-scale feature integration in the traditional Vision Transformer model, which significantly improve the performance and efficiency of the model. In our experiments, we comprehensively validate the SpemNet model on the CottonInsect dataset, and the results show that SpemNet performs well in the cotton pest recognition task, with significant effectiveness and superiority. The SpemNet model excels in key metrics such as precision and F1 score, demonstrating significant potential and superiority in the cotton pest and disease recognition task. This study provides an efficient and reliable solution in the field of cotton pest and disease identification, which is of great theoretical and applied significance.