# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
## 1 Introduction  
According to the International Organization of Vine and Wine (OIV), the top three wine-producing countries in 2023 were France, Italy and Spain, with production reaching 45.8 million liters, 43.9 million liters and 30.7 million liters. Wine production is high and wine grapes are cultivated on a large area. In order to ensure neat budding in the spring and high-quality grape berries, grapevines planted in northern regions need to be pruned in the winter, cutting off excess buds on the branches before burying in soil to resist the cold [1]. The mainstream pruning methods currently on the market include manual pruning and machine pruning; manual pruning is effective, but the labor demand is large, with high pruning costs; machine pruning [2] depends on the bundled branches of the cordon; the efficiency is higher, but the effect of pruning is poor, there is a tendency to miss, there are pruning errors, etc., which results in the quality of wine grapes being greatly reduced. For the pruning of high-quality wine grapes, such as Cabernet Sauvignon wine grapes, which is the object of research in this paper, this paper proposes an intelligent pruning system based on images, which can be pruned in the appropriate position according to the pruning rules, reducing labor costs while ensuring high pruning efficiency and good pruning results. Up to now, there have been studies aimed at recognizing fruits and stems of various fruit trees. [PERSON] et al. [3] fed both pseudo-color and depth images into a convolutional neural network R-CNN to detect branches of apple fruit trees, which was used to automatically shake the branches to harvest apple fruits. [PERSON] et al. [4] used U-Net and DeepLabv3 and a conditional generative adversarial network, Pix2 Pix, to recognize occluded apple trees growing in a V-shape. [PERSON] et al. [5] used a deep learning method (SPGNet) to segment a 3D point cloud model of date palm trees and estimated the number of date palm branches using the DBSCAN clustering algorithm. [PERSON] et al. [6] used Deeplabv3 to segment the RGB image of lychee nests using a skeleton extraction operation and a noise-based nonparametric density spatial clustering method to identify fruiting branches belonging to the same lychee cluster. [PERSON] et al. [7] used Mask R-CNN and branch segment merging algorithms to detect citrus fruits and branches in the presence of foliage occlusion and mapped color images onto depth images to obtain the diameters of fruits and branches. [PERSON] et al. [8] used the lightweight target-detection network YOLOv7-tiny on citrus in video technique, which can measure the citrus yield in an orchard. [PERSON] et al. [9] introduced RGB-D images and used MSEU R-CNN to segment citrus canopies to obtain the growth information of the canopies, which provided a method for spraying robots. In the study by [PERSON] et al. [10], in order to pick citrus fruits at the time of obtaining picking points on branches, a candidate region was first generated and then a multilevel feature fusion point was proposed to determine the picking points in the candidate region. In the study by [PERSON] et al. [11], in order to prune a rose bush for its 3D reconstruction, a full convolutional neural network was used to obtain the skeleton and branching structure of the plant after segmentation of the branches. In the study by [PERSON] et al. [12], in order to collect the phenotypic traits of a plant population, RoseSegnet, a deep learning network based on a 3D point cloud, was proposed for segmentation of the organs of the rose plant. [PERSON] et al. [13] developed a new architecture called \"Mango YOLO\" based on the features of YOLOv3 [14] and YOLOv2 (tiny) [15]. [PERSON] et al. [16] used Dense-YOLOv4 to detect mangoes at different growth stages under occlusion, and also used DenseNet with a backbone to optimize feature transmission and a Path Aggregation Network (PANet) with an improved pathway to retain fine-grained localization information. [PERSON] et al.