基于改进UNet模型的核桃树枝条分叉点定位与修剪位置选择  

Locating branch bifurcation points to select the pruning position of walnut trees using improved UNet model

作　　者：王志富 马文强[2] 项斌斌 杨莉玲[2] 沈晓贺[2] 刘佳[2] WANG Zhifu;MA Wenqiang;XIANG Binbin;YANG Liling;SHEN Xiaohe;LIU Jia(College of Mechanical Engineering,Xinjiang University,Urumqi 830049,China;Agricultural Mechanization Institute,Xinjiang Academy of Agricultural Sciences,Urumqi 830091,China)

机构地区：[1]新疆大学机械工程学院,乌鲁木齐830049 [2]新疆农业科学院农业机械化研究所,乌鲁木齐830091

出　　处：《农业工程学报》2025年第7期165-172,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：新疆维吾尔自治区自然科学基金面上项目(2022D01A261);国家自然科学基金地区基金项目(32460436);中央财政林草科技推广示范项目(新2024TG21);新疆农业科学院青年科技骨干创新能力培养项目(xjnkq-2022016)。

摘　　要：为定位核桃树枝条分叉点,筛选枝条修剪位置,该研究提出了基于改进UNet模型与图像处理的枝条分叉点定位与修剪位置选择方法。首先,改进语义分割模型,VGG16-UNet中添加MCA(multidimensional collaborative attention)模块,命名为VMfd-UNet,实现核桃树树干和枝条的识别与分割;其次,提取掩膜细化处理,根据枝条相交情况、相对位置关系,结合图像处理与深度信息定位枝条分叉点;最后,以k-means聚类枝条分叉点三维坐标的欧几里得距离计算出质心,取距离质心最近的枝条分叉点作为修剪位置。试验表明,VMfd-UNet在整个数据集上的平均像素精度m PA和平均交并比mIoU分别比VGG16-UNet高4.86、4.85个百分点,在验证集上表现优异,树干和枝条的mPA分别达到96.71%和90.42%,mIoU分别达到90.27%和79.86%,参数量为35.73 M。以枝条分叉点选择修剪位置,平均准确率达到83.2%。该研究可为核桃树准确定位修剪点提供位置参考与技术支持。Walnut trees are characterized by their tall stature,numerous branches,and complex structures,making it challenging to directly determine the spatial positions of pruning points.Pruning points are typically determined based on the branch bifurcation points,along with branch types and corresponding pruning amounts to establish specific locations.Therefore,accurately locating the bifurcation points of walnut tree branches is a critical aspect of ensuring precise pruning point localization.This study proposes a method for locating branch bifurcation points and selecting pruning positions for walnut trees,based on an improved UNet model and image processing techniques.First,data collection,augmentation,and enhancement were conducted,followed by detailed annotation of walnut tree trunks and branches to construct the dataset.The dataset was divided into training and validation sets in a 9:1 ratio,containing 3168 and 352 images,respectively.Subsequently,a semantic segmentation model suitable for this dataset was selected.Specifically,the Multidimensional Collaborative Attention(MCA)module was integrated into the VGG16-UNet architecture to fuse detailed feature outputs with the corresponding layer’s decoding operations.The model,named VMfd-UNet,employed focal loss and dice loss as loss functions for the training and validation sets,respectively.Experimental results demonstrated that VMfd-UNet achieved mean pixel accuracy(mPA)and mean intersection over union(mIoU)on the entire dataset that were higher by 4.86 and 4.85 percentage points,respectively,compared to the VGG16-UNet model.The VMfd-UNet performed exceptionally well on the validation set and outperformed other models utilizing different attention mechanisms.The mPA of the trunk and branches reached 96.71%and 90.42%,while the mIoU values were 90.27%and 79.86%,respectively.The model’s parameter count was 35.73M,and its computational cost was 156.93G.The improved model demonstrates excellent detection performance and robustness,capable of better segmenting walnut

关 键 词：图像处理 核桃 修剪 UNet模型 分叉点 定位 

分 类 号：S24[农业科学—农业电气化与自动化]