## 6 Conclusions  
1. A multi-target tomato plant identification model was developed. By using weights pretrained on the large ImageNet dataset to train the tomato plant segmentation model, the convergence speed of the model was accelerated. The model achieved a mean intersection over union (_MloU_) of 85.33% and a mean pixel accuracy (_mPA_) of 92.47%, an improvement of 5.02% and 4.08%, respectively, over the VGG16-UNet model without pretrained weights. In addition, the model achieved intersection-over-union and pixel accuracies of 75.91%, 78.86%, 87.01%, and 87.47%, 89.53%, 93.15% for all the segmentation categories (axillary branch, lateral branch, and main branch), respectively. These results show an improvement of 10.22%, 6.73%, and 2.92% for _MloU_ and 8.88%, 5.55%, and 1.8% for _mPA_ compared to the VGG16-UNet model without pretrained weights, allowing the accurate identification of the main, lateral, and axillary branch regions of tomato plants.
2. Implementation of the calculation of axillary bud removal points was achieved. Building on the multi-objective segmentation of the tomato plants in VGG16-UNet, the axillary bud region of the tomatoes was identified by HSV color space conversion and the selection of color threshold ranges. Morphological dilation and erosion operations were used to remove noise and connect adjacent regions of the same target. The left and right endpoints and the centroid of the axillary buds were determined using a feature point extraction algorithm. The left and right positions of the axillary buds were then determined based on the relationship between the position of the axillary bud centroid and the position of the main branch. Finally, the axillary bud removal points were calculated using feature points based on the relationship between the axillary bud positions and the branch position.
3. Two criteria were used to determine the accuracy of axillary bud removal points. By correlating real lengths with pixels in the images, removal points located within 150 pixels of the axillary bud pruning point, near the end of the main branch, were considered to meet the pruning length requirements. After experiments on 162 test set images, the accuracy of the axillary bud removal point localization reached 85.5%.  
The next plan is to optimize the model for efficient deployment on edge computing devices. This will involve using network compression techniques or integrating advanced lightweight network architectures such as MobileNet and DenseNet to reduce the size  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rules** & **Actual Number** & **Eligible** & **Not Eligible** & **Accuracy (\%)** \\ \hline Rules 1 & 701 & 643 & 58 & 91.7 \\ Rules 2 & 701 & 653 & 48 & 93.2 \\ Overall & - & - & - & 85.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Axillary bud removal point identification results.  
of the model while maintaining detection accuracy. These enhancements are critical for the practical application of technologies in areas such as intelligent agriculture, and will contribute to the automation and intelligence of agricultural production, thereby promoting the development of sustainable agriculture.  
Author Contributions: Supervision, J.F. and Y.Z.; conceptualization, J.F., Y.Z. and X.L.; formal analysis, X.L. and J.F.; methodology, X.L.; software, X.L.; writing--original draft, X.L. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ongoing study.  
The authors declare no conflicts of interest.