## 1 Introduction  
With the advancement of agricultural science and technology, improving the efficiency of monitoring and managing the growth status of crops is crucial for the agricultural industry. Among the world's most widely grown vegetables, tomatoes stand out because the growth of their shoots has a significant impact on both yield and quality, particularly the axillary buds that develop between the main stem and the base of the lateral branches. However, the current method of removing axillary buds relies on manual labor, resulting in significant labor requirements and high production costs, especially in large-scale cultivation [1, 2]. The wide row spacing typical of large-scale tomato production makes the automation of axillary bud removal feasible [3]. Therefore, the development of a tomato axillary bud remover robot to replace manual labor is not only feasible but imperative. To achieve automated tomato axillary bud pruning, the accurate detection of tomato main branches, side branches, and axillary buds and the identification of pruning points are of paramount importance. Currently, scholars have conducted relevant research on the automatic pruning of plant branches and fruit and vegetable harvesting. In 2017, the Dutch company Priva released a tomato branch and leaf-pruning robot, which can achieve the automatic pruning of tomato branches and leaves [4]. [PERSON] et al. [5] improved Mask R-CNN to identify and segment grape stems, achieving an average recognition accuracy of 88% for grape stems. [PERSON] et al. [6] used the K-means clustering method to identify the branches of Lycium barbarum, overcoming the problem of numerous interferences in the identification of wolf-berry branches in natural environments. [PERSON] et al. [7] used DeepLabv3+ to segment lithchi branches and obtained an average intersection over union ratio of 76.5% for model segmentation. [PERSON] et al. [8] proposed an improved deep learning model, RDf-DeepLabV3+, to address the semantic segmentation of lychee branches in an orchard environment. The main improvement of this model is the introduction of a new backbone network, called ResDense, which integrates the ResNet and DenseNet networks and replaces the original backbone network of DeepLabV3+. In addition, focal loss is used to replace the original cross-entropy loss function. Experimental results show that the RDf-DeepLabV3+ model outperforms all comparison models. Specifically, the mean intersection over union (mIoU) of the RDF-DeepLabV3+ model is 0.848, 0.811, and 0.770 for images of simple, moderate, and complex levels, respectively. Furthermore, the training and testing speeds of this model are approximately 7.7% faster than Xception. This research provides a reliable deep learning solution for the accurate segmentation of lychee branches in orchard environments, which is of great practical importance for robotic lychee harvesting. [PERSON] et al. [9] combined VGG19 with Segnet for the detection and segmentation of grape berries, achieving F1 scores of 0.93 and 0.73, respectively. [PERSON] et al. [10] built a Mask R-CNN-based model for the identification of ripe and urnire tomatoes, with recognition accuracies of 95% and 94%, respectively. [PERSON] et al. [11] used the DA2-YOLOv4 model to identify hedges with a recognition speed of 83.1 frames per second and an average accuracy of 98.5%. [PERSON] et al. [12] used an improved Mask R-CNN model to segment rice stalk contamination, achieving a segmentation and detection accuracy of 91.12% with an average processing time of 3.57 s. [PERSON] et al. [13] used a fusion of YOLOv3 and UNet to detect and segment lychee and fruit stalks in a nocturnal environment, demonstrating high accuracy and robustness. [PERSON] et al. [14] proposed an improved apple-picking robot vision detector model based on a modified Mask R-CNN. They combined ResNet and DenseNet as the backbone network for feature extraction, aiming to reduce input parameters while retaining valuable features for accurate detection.