## 3 Results and Discussion
### Test Result Analysis
#### 3.3.2 MSEU R-CNN Instance Segmentation Performance Test  
To understand the performance of the MSEU R-CNN in the tree-crown segmentation task in a variety of complex backgrounds, Dataset 1 was used for model training, and citrus crown images from three growth periods were selected for testing (i.e., 200 images: 65 seedlings, 66 flourishing, and 69 fruiting). The background of each period was complex and varied. The performance of the model varies greatly in different equipment tests. To evaluate the trained model accurately, the same hardware configuration used during training was applied. The main test equipment was a high-performance computer employing a GeForce RTX2060 GPU (6 GB), an Intel i7 9750h CPU, and an SSD (100G) mainly. The fruit trees at each stage had different shapes, which differently affected the prediction results of the instance segmentation model. The MSEU R-CNN model results are shown in Table 4.  
It can be seen from Table 4 that the bbox and seg AP50 of the MSEU R-CNN were 99.5 and 99.7%, respectively, which were highest of the three stages. The detection and segmentation accuracies for the seedling and fruiting stages were no more than 96%. According to the differential analysis of the crown growth stage, it can be seen that owing to the large surface areas of the citrus during the flourishing period, sufficient feature information was obtained during feature extraction, further stabilizing the training effects and enabling faster convergence. Nevertheless, the surface areas of the seedling citrus trees were small, and the characteristic information was insufficient. The training loss was therefore difficult to converge to lower levels, and the final prediction effects were poor. Although the surface areas were largest during the fruit-bearing period, and the characteristic quantities were sufficient, the surrounding weeds and other background growth were dense, causing strong interference. Owing to the great similarity between the target object and complex background features, model training was constrained, making it difficult to distinguish between contours. However, from the final test results, the average bbox AP50 of MSEU R-CNN model detection was 96.6%; that of segmentation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluating Indicator** & **Seedling** & **Flourishing** & **Fruiting** & **Average Value** \\ \hline (Bbox) AP50 (\%) & 95.2 & 99.5 & 95.1 & 96.6 \\ (Bbox) AR (\%) & 66.8 & 82.0 & 70.3 & 73.0 \\ (Seg) AP50 (\%) & 95.6 & 99.7 & 93.1 & 96.2 \\ F1-score (\%) & 62.7 & 80.0 & 65.2 & 69.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Instance segmentation performance test results of MSEU R-CNN model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **(Bbox)AP** & **(Bbox)AR** & **(Seg)AP** & **F1-Score** \\ \hline No using RGB-D & 0.5792 & 0.6540 & 0.5737 & 0.6143 \\ Using RGB-D & 0.5809 & 0.6793 & 0.5747 & 0.6263 \\ Promotion ratio & 0.3\% & 4.0\% & 0.1\% & 2.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Validity analysis of RGB-D image.  
was 96.2%, and the recall rate was 73.0%, indicating that the overall instance segmentation performance was excellent. These experiments show that the MSEU R-CNN instance segmentation model effectively resolves the detection and segmentation problems of citrus crowns evaluated at different growth stages in unstructured environments.