## 1 Introduction
The results show that the model achieved a precision of 97.31% and a recall of 95.70%, meeting the requirements of the practical apple-picking robot vision system in orchard environments. [PERSON] et al. [15] segmented tomato plants using the improved Mask R-CNN model and achieved the positioning of tomato lateral branch pruning points through mask merging and conditional constraints. The average accuracy of pruning point identification was 82.9%. The above studies indicate some progress in the field of plant pruning and fruit and vegetable harvesting. However, research into the identification of tomato axillary pruning points is limited, with traditional image processing methods being predominantly used. Traditional image processing methods and convolutional neural network detection techniques are common approaches to target recognition. Due to the similar colors of tomato lateral branches, main branches, and axillary buds, traditional threshold segmentation methods perform adequately in certain scenarios. However, they are sensitive to variations in plant state and growth environment, resulting in limited detection capabilities in complex and changing growth environments. In contrast, deep learning models can effectively detect the main branch, lateral branch, and axillary bud targets of tomato plants by leveraging multi-dimensional features such as color, shape, and texture. These models demonstrate adaptability to different scenarios and changes in plant status, thereby improving the ability to detect multiple targets of tomato plants in dynamic growth environments. To address the challenge of effectively segmenting axillary buds from a background of similar colors using traditional image processing methods and to ensure robust recognition in dynamic environments, this paper proposes a methodology. We use the VGG16-UNet deep learning model to achieve the segmentation of tomato plant main branches, lateral branches, and axillary buds. In addition, we use techniques such as HSV color space conversion and threshold segmentation to achieve fine segmentation and separation of the main branches, lateral branches, and axillary buds. Feature points are then extracted from the segmented regions. Subsequently, the axillary bud growth position is determined based on these feature points, and the tomato axillary bud pruning point is calculated using positional relationships. This approach aims to provide technical support for the identification of pruning points by tomato axillary bud-picking robots. The main contributions and innovations of this study are outlined as follows:  
* Proposal of a multi-target segmentation method for tomato plants based on the VGG16-UNet model. This method effectively performs pixel-level segmentation of the main stem, lateral branches, and axillary buds of tomato plants, providing a solid basis for calculating axillary bud removal points for automated agricultural robots. * By combining the powerful VGG16 and ResNet models with the UNet architecture and using pretrained weights, the accuracy and training efficiency of the model for tomato plant image recognition is significantly improved. This fusion enhances feature extraction capabilities and improves model generalization, enabling better adaptation to complex agricultural scenarios. * Development of a fine segmentation algorithm that combines HSV color space transformation and morphological operations. This algorithm effectively separates key structures of tomato plants from complex backgrounds, improving the accuracy of axillary bud positioning and providing reliable technical support for automated agricultural robots. * The use of traditional semantic segmentation evaluation metrics alongside the introduction of pixel-length correspondence analysis and specific rule evaluations. This approach provides a new perspective for comprehensive model evaluation, ensuring the effectiveness and reliability of the model in practical applications. The remainder of this paper is divided into five sections.