## 3 Results and Discussion
### Test Result Analysis
#### 3.3.3 Effectiveness Analysis of Model Structure Optimization  
The proposed MSEU R-CNN model integrates SE blocks in its backbone structure. To verify its improved feature extractability, ResNet-18, ResNet-50, ResNet-101, and ResNext-50 [53] methods were combined with various Mask R-CNN models with FPN backbones for comparison. The structures of different models for segmenting citrus crown are shown in Table 5. Additionally, a comparison experiment of the model before the mask-branch improvement was added to verify the segmentation improvement of the Unet++. These models were trained and verified, by using Dataset 1. The bbox and seg AP50, AR, F1, MioU50, and reasoning scores are plotted in Table 6. From this, the MSEU R-CNN citrus crown segmentation model is shown to have an AP50 of 96.6%, which is at least 3.4% better than other models. The seg's AP was 96.2%; AR was 73.0%; F1-score was 69.4%, and the MioU50 was 74.2%, all higher than the other models. Although the inference speed of the proposed model did not change much (i.e., the average running time was 0.19 s, and the average frame rate was \(\sim\)5 fps), the accuracy was significantly improved. Models 5 and 6 represent the conditions before and after the mask-branch improvement. From Table 6, it can be seen that the optimized seg AP50 and MioU50 of the mask branch reached 96.2% and 74.2%, which are 2.6% and 1.3% higher than before the improvement, respectively. This shows that the introduction of the Unet++ semantic segmentation module had a significant effect in improving the segmentation accuracy. Models 1 and 5 reflect the comparison before and after the ResNet optimization. From the data of Table 6, it can be seen that the AP50, AR, and F1-score of the bbox model after feature extraction increased by 0.6, 2.3, and 2.8%, respectively, compared with those before improvement. The ResNet layers of Models 2, 3, and 4 were deeper than those of Model 5. However, because Model 5 is embedded in the SE block, the detection accuracy index was the highest, and the feature extractability was the strongest. The comparisons between models 1-5 showed that the SE block made the SE-ResNet pay more attention to learning effective features (e.g., citrus crowns during feature-map generation while readjusting the feature channel weight value and effectively suppressing the complex background features of orchards through the network self-attention). Thus, we solved the problem of poor recognition accuracy when the crown is densely distributed. Model 5 uses a more lightweight ResNet-18 network than is conducive to improving model efficiency. The detection time of a single image is 0.19 s. Therefore, the MSEU R-CNN model with the SE-ResNet-18-FPN backbone is more robust in segmenting citrus crowns by comprehensively weighing the average precision and running speeds. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Model** & **Backbone** & **Mask Branch** \\ \hline Model 1 & Mask R-CNN & ResNet-18-FPN & FCN \\ Model 2 & Mask R-CNN & ResNet-50-FPN & FCN \\ Model 3 & Mask R-CNN & ResNet-101-FPN & FCN \\ Model 4 & Mask R-CNN & ResNet-50-FPN & FCN \\ Model 5 & Mask R-CNN & SE-ResNet-18-FPN & FCN \\ Model 6 & MSEU R-CNN & SE-ResNet-18-FPN & Unet++ \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Different model structures on citrus crown segmentation. Figure 9 compares the segmentation results of the different models at different growth stages. The differences in model segmentation results can be seen in the blue boxes. In Figure 9, the masks generated by the crown segmentations of each stage of Models 1-5 are missing or redundant, whereas the segmentation mask, MSEU R-CNN, is closer to the label diagram, indicating that the model's segmentation effect is obviously better than Model 1-5.