# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 3 Results and Discussion
### Evaluation Metrics
#### 3.2.1 Comparison of Model Classification Accuracy  
To verify the effectiveness of AMMFNet in apple disease and pest diagnosis, under the same experimental conditions, the constructed apple disease and insect pest multi-source canopy image dataset was used to compare the experimental performance of AMMFNet with other classification models. The experimental results show that the model accuracy is the best when the AMMFNet feature extraction network uses ResNet-18. Therefore, the feature extraction networks of AMMFNet in the comparative experiments all used ResNet-18. The comparison models used in the experiment included the multi-category classification model residual error attention network (Attention-92) [60] and NFNet [61] and the multi-label classification model Query2 Label [62]. All the comparison models used a combination of single-source RGB images and RGB + VIs as inputs to verify the improved performance of AMMFNet relative to single-source input prediction and the effectiveness of multi-source feature fusion.  
The experimental results are shown in Tables 5 and 6. The experimental results show that the accuracy of AMMFNet proposed in this paper is significantly higher than that of all the models using single-source input RGB images, and at the same time, it has lower  
\begin{table}
\begin{tabular}{c c} \hline
**Configuration Item** & **Value** \\ \hline CPU & Intel(R) Xeon(R) CPU E5-2650 v3 (Intel, Santa Clara, CA, USA) \\ GPU & NVIDIA Tesla T4 16 GB (NVIDIA, Santa Clara, CA, USA) \\ CUDA & 11.3 \\ Operating system & Ubuntu 18.04.2 LTS (64-bit) \\ Memory & 128 GB \\ Hard drive & 2 TB \\ Deep learning framework & PyTorch 1.11.0 \\ Language & Python3.8 \\ \hline \end{tabular}
\end{table}
Table 4: Experimental setup.  
computational overhead. AMMFNet performs significantly better in accuracy than models that use only a single RGB input. When using RGB + VIs combined input, although it should theoretically provide more abundant information, not all models can effectively integrate these multi-source data. Among them, due to its excessive dependence on the attention mechanism, the Attention-92 model pays too much attention to redundancy data, which fail to effectively learn and integrate multi-source features, and its performance is even lower than that of the model using only RGB input. At the same time, each attention module of Attention-92 needs to perform calculations such as weight generation and feature recalibration, which takes up a large amount of computational resources and memory, resulting in a large forward pass size. The Query2 Label model, due to the computational complexity of its self-attention mechanism, has a significantly higher forward pass size and parameter size than other models. In comparison, AMMFNet, with a smaller forward pass and parameter size, not only excels among all the models using combined inputs but also surpasses the high-performing NFNet model by 4.7% in subset accuracy. Although Weight Standardization is used in NFNet to replace the traditional BN layer, due to the large distribution differences between data channels, this standardization method has limited effect in processing multi-source data. However, before feature extraction, AMMFNet performs global feature selection on the input data, effectively enhancing the fusion of multi-source data and achieving higher model performance. In addition, AMMFNet is also more accurate than other models in predicting single apple diseases and insect pests. This shows that the proposed model can effectively fuse multi-source data and improve the accuracy of apple pest classification.