# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 3 Results and Discussion
### Evaluation Metrics
#### 3.2.4 Impact of Input Data on Model Performance  
To verify the effectiveness of the calculated vegetation indices combination, this experiment trained ResNet series models on RGB, multispectral, and vegetation indices datasets, respectively. The universal effectiveness of the selected vegetation indices was validated through ResNet18, ResNet34, ResNet50, and ResNet101.  
The experimental results are shown in Table 10. The results show that the accuracy of the proposed vegetation indices combination on the four models is higher than that of the RGB and multispectral images. Especially when the model structure is simpler, such as ResNet18, the vegetation indices combination has greater performance improvement. The reason is that a simpler model structure often implies fewer parameters and less computational load. Therefore, the model can quickly learn effective feature representations from the vegetation indices representing the physiological information of apples. In contrast, complex models, due to their stronger learning capabilities, may fit unnecessary noise.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Input Image** & **Subsetac/\%** & **Accuracy/\%** & **Recall/\%** & **Precision/\%** & **F1/\%** \\ \hline \multirow{3}{*}{ResNet18} & RGB & 83.99 & 78.26 & 78.53 & 79.92 & 79.22 \\  & MS & 82.02 & 76.34 & 77.69 & 77.59 & 77.64 \\  & VIs & **87.58** & **80.71** & **82.05** & **81.97** & **81.90** \\ \hline \multirow{3}{*}{ResNet34} & RGB & 82.38 & 77.64 & 78.06 & 79.83 & 78.93 \\  & MS & 83.10 & 77.37 & 78.04 & 79.38 & 78.70 \\  & VIs & **86.20** & **80.15** & **80.95** & **81.93** & **81.44** \\ \hline \multirow{3}{*}{ResNet50} & RGB & 83.83 & 78.56 & 79.56 & 81.04 & 80.29 \\  & MS & 85.80 & 79.51 & 80.10 & 81.20 & 80.72 \\ \cline{1-1}  & VIs & **86.40** & **80.28** & **80.97** & **82.09** & **81.53** \\ \hline \multirow{3}{*}{ResNet101} & RGB & 86.61 & 81.14 & 81.85 & 83.01 & 82.50 \\ \cline{1-1}  & MS & 87.32 & 80.67 & 81.43 & 82.05 & 81.74 \\ \cline{1-1}  & VIs & **89.26** & **82.29** & **82.83** & **83.61** & **83.22** \\ \hline \hline \end{tabular}  
* The bold content is the column optimal value.  
\end{table}
Table 10: Classification results among RGB, multispectral (MS), and vegetation indices (VIs).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **RGB** & **VIs** & **RF-ML** & \
\begin{tabular}{c} **Channel** \\ **Attention** \\ \end{tabular} &
\begin{tabular}{c} **Data-Level** \\ **Fusion** \\ \end{tabular} & **Subsetac/\%** \\ \hline \multirow{4}{*}{ResNet18} & ✓ & & & & 83.99 \\  & & ✓ & & & 87.58 \\  & ✓ & ✓ & & & 86.40 \\  & & ✓ & ✓ & & 88.14 \\  & ✓ & ✓ & ✓ & & ✓ & 90.20 \\  & ✓ & ✓ & ✓ & ✓ & ✓ & **92.92** \\ \hline \hline \end{tabular}  
* / represents the items retained in the ablation experiment.  
\end{table}
Table 9: Ablation experiment.  
Hence, vegetation indices are more effective on shallower networks. Compared with the model trained with RGB images, the accuracy of the subset of ResNet18 trained with vegetation indices increased by 3.59%, and the F1-score increased by 2.68%.