多尺度自注意力特征融合的茶叶病害检测方法

针对茶叶病害检测面临的病害尺度多变、病害密集与遮挡等诸多问题，提出了一种多尺度自注意力茶叶病害检测方法(Multi-scale guided self-attention network, MSGSN)。该方法首先采用基于VGG16的多尺度特征提取模块，以获取茶叶病害图像在不同尺度下的局部细节特征，例如纹理和边缘等，从而有效表达多尺度的局部特征。其次，通过自注意力模块捕获茶叶图像中像素之间的全局依赖关系，实现病害图像全局信息与局部特征之间的有效交互。最后，采用通道注意力机制对多尺度特征进行加权融合，提升了模型对病害多尺度特征的表征能力，使其更加关注关键特征，从而提高了病害检测的准确性。实验结果表明，融合多尺度自注意力的茶叶病害检测方法在背景复杂、病害尺度多变等场景下具有更好的检测效果，平均精度均值达到92.15%。该方法可为茶叶病害的智能诊断提供参考依据。

Tea Disease Detection Method with Multi-scale Self-attention Feature Fusion

Accurate detection of tea diseases is crucial for a high yield and quality of tea, thereby increasing production and minimizing economic losses. However, tea disease detection faces several challenges, such as variations in disease scales and densely occluded disease areas. To tackle these challenges, a novel method for detecting tea diseases called multi-scale guided self-attention network (MSGSN) was introduced, which incorporated multi-scale guided self-attention. The MSGSN method utilized a VGG16-based module for extracting multi-scale features to capture local details like texture and edges in tea disease images across multiple scales, effectively expressing the local multi-scale features. Subsequently, the self-attention module captured global dependencies among pixels in the tea leaf image, enabling effective interaction between global information and the disease image''s local features. Finally, the channel attention mechanism was employed to weight, fuse, and prioritize the multi-scale features, thereby enhancing the model''s ability to characterize the multi-scale features of the disease and improving disease detection accuracy. Experimental results demonstrated the MSGSN method''s superior detection performance in complex backgrounds and varying disease scales, achieving an accuracy rate of 92.15%. This method served as a valuable reference for the intelligent diagnosis of tea diseases. In addition, the method can provide a scientific basis for the prevention and control of tea diseases and help farmers take timely and effective control measures. At the same time, the method can also provide technical support for the development of the tea industry.