## 3 Materials and Methods
### CG Attention Module  
Soil, branches, and trunks are coexisting with tea leaves, creating a complex and diverse growth environment for tea plants. The complex environmental background may interfere with the extraction of key disease information during the feature extraction process, leading to the neglect or loss of some important features. Therefore, by incorporating an effective attention mechanism into the network model, the weight of important features can be increased, thereby enhancing the tea disease features and sup-pressing irrelevant background features. This can improve the efficiency of extracting disease features from images. CG Attention is an efficient attention mechanism designed to enhance the computational efficiency and diversity of attention mechanisms in vision Transformers, while simultaneously reducing the computational redundancy in Multi-Head Self-Attention (MHSA) [34] modules. By introducing feature grouping and cascaded processing strategies, CG Attention effectively alleviates the issue of attention map similarity in traditional MHSA and significantly strengthens the model's representational capacity. Figure 2: The structure of the Faster-LTNet network. In MHSA, the input features are mapped to multiple independent attention subspaces to capture global dependencies. However, traditional MHSA provides the same input features to all attention heads, resulting in highly similar feature maps learned by different heads, thus wasting computational resources and limiting the diversity of feature representations. To address this issue, CG Attention provides independent feature splits for each attention head, explicitly decomposing the attention computation and significantly enhancing the diversity of attention maps. The detailed structure of the CG attention module is depicted in Figure 3.  
The core mechanism of CG Attention includes grouped feature input and cascaded design. Specifically, CG Attention divides the input feature \(X_{i}\) into h sub-feature groups \([X_{i1},X_{i2},\dots,X_{ih}]\), with each group being processed independently by a corresponding attention head. This design, akin to group convolutions in convolutional networks, significantly minimizes parameter count and reduces computational complexity. Unlike traditional MHSA, CG Attention's grouping strategy reduces the number of input and output channels by a factor of 1/h, thereby reducing the computational overhead of matrix multiplications while improving attention diversity. The computation for each attention head is given by the following:  
\[\widetilde{X}_{ij}=Attn\Big{(}X_{ij}W_{ij}^{\text{Q}},X_{ij}W_{ij}^{\text{K}},X _{ij}W_{ij}^{\text{V}}\Big{)} \tag{1}\]  
where \(\widetilde{X}_{ij}\) is the output feature of the \(j\)-th attention head, and \(W_{ij}^{\text{Q}}\), \(W_{ij}^{\text{K}}\), and \(W_{ij}^{\text{V}}\) are the linear transformation matrices used to project the input features. To further enhance feature representation, CG Attention introduces a cascaded operation between attention heads. Specifically, the output of each attention head is passed to the next head, added to its input features, and progressively refines the feature represen  
Figure 3: CG attention module structure diagram. tation. This process increases the network depth and non-linear expressiveness, without introducing additional parameters. The cascaded computation is expressed as follows:  
\[X^{\prime}_{ij}=X_{ij}+\widetilde{X}_{i(j-1)},\ \ \ \ 1<j\leq h \tag{2}\]  
where \(\widetilde{X}_{i(j-1)}\) is the output of the previous attention head. This mechanism not only enhances the network depth but also improves the utilization of previous layer information by the attention heads. Through the feature grouping and cascaded strategies, CG Attention innovatively addresses the issues of computational redundancy and capacity limitations in MHSA. Unlike conventional attention mechanisms that calculate dense pairwise interactions across all features, the CG Attention Module simplifies this process by employing lightweight operations.