# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 3 Results and Discussion
### Evaluation Metrics
#### 3.2.2 Effectiveness Analysis of Multi-Source Fusion Prediction Model  
This section focuses on validating the effectiveness of AMMFNet by comparing its prediction accuracy with models trained using three different data sources: visible light images, multispectral images, and calculated vegetation indices. Additionally, this experiment also evaluates the classification accuracy of the AMMFNet model for individual categories to verify its effectiveness in identifying specific apple pest and disease categories. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Image** & **Model** & **Aphid** & **Alternaria** & **Mosaic** & **Brown Spot** & **Gray Spot** \\ \hline \multirow{3}{*}{RGB} & Attention-92 & 68.51 & 87.31 & 99.60 & 72.02 & 86.33 \\  & NFNet & 94.91 & 97.92 & 100.0 & 89.60 & 96.82 \\  & Query2 Label & 94.95 & 97.31 & 100.0 & 90.59 & 96.45 \\ \hline \multirow{3}{*}{RGB + VIs} & Attention-92 & 73.82 & 84.54 & 99.41 & 72.33 & 81.60 \\  & NFNet & 96.50 & 98.59 & 100.0 & 95.11 & 97.67 \\ \cline{1-1}  & Query2 Label & 95.81 & 98.33 & 99.90 & 93.86 & 97.17 \\ \cline{1-1}  & **AMMFNet** & **97.54** & **99.01** & **100.0** & **97.41** & **98.47** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of single-class disease and pest classification accuracy (%) among different classification models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Input Image** & **Model** & **Substec/\%** & **Accuracy/\%** & **Recall/\%** & **Precision/\%** & **F1/\%** & **Forward Pass Size (MB)** & **Params Size (MB)** \\ \hline \multirow{3}{*}{RGB} & Attention-92 & 39.19 & 61.29 & 89.71 & 61.30 & 72.81 & 723.53 & 192.37 \\  & NFNet & 80.04 & 75.61 & 76.62 & 77.11 & 76.92 & 199.65 & **161.76** \\  & Query2 Label & 79.71 & 77.95 & 80.14 & 79.91 & 79.95 & 924.47 & 367.24 \\ \hline \multirow{3}{*}{RGB + VIs} & Attention-92 & 35.49 & 59.51 & 88.80 & 59.73 & 71.36 & 723.53 & 192.64 \\  & NFNet & 88.18 & 82.00 & 82.69 & 83.60 & 83.11 & 199.65 & 162.03 \\ \cline{1-1}  & Query2 Label & 86.16 & 81.49 & 82.89 & 83.22 & 83.10 & 924.47 & 367.51 \\ \cline{1-1}  & **AMMFNet** & **92.92** & **85.43** & **85.89** & **86.54** & **86.21** & **192.75** & 164.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of apple disease classification performance and computational complexity among different classification models. As shown in Table 7, the RGB, MS, and VIs in the table represent the RGB image, the multispectral image, and the calculated vegetation indices, respectively, as the input model recognition result obtained. The experiments show that AMMFNet is superior to single-source input on ResNet18, ResNet34, and ResNet50, because RGB images and multispectral images carry different features, and these features are all conducive to pest classification. AMMFNet effectively uses the complementary information in the RGB and multispectral images taken by the UAV to form an effective feature representation. Among them, the recognition model using ResNet18 as the feature extraction network performed best, achieving a 92.92% subset accuracy, 85.43% sample accuracy, and 86.21% F1 score. This is because both the vegetation indices themselves and the multi-source data after feature selection and fusion already have more significant pest-sensitive characteristics. Compared with the deeper network structure, ResNet18 can converge faster and learn these significant data characteristics, but it will not be disturbed by noise in the high feature dimension. In addition, the subset accuracy of AMMFNet exceeds the subset accuracy of single-source RGB images, multispectral images, and vegetation indices. The accuracy rate of AMMFNet for five types of apple diseases and insect pests is shown in Table 8. It can be found that the accuracy rate of AMMFNet for each type of disease and insect pest is higher than 97%. Among them, the recall rate of brown spot disease is the highest, reaching 95.6%. The recall rate of mosaic disease is the lowest.