农业机械学报2026，Vol.57Issue(1)：92-103,12.DOI:10.6041/j.issn.1000-1298.2026.01.009
基于图结构引导的稻穗骨架解析与关键表型参数无损测量方法
Graph Structure-guided Rice Panicle Skeleton Parsing and Non-destructive Measurement of Key Phenotypic Parameters
摘要
Abstract
The high-throughput and non-destructive acquisition of panicle phenotypic parameters is a key step in rice breeding and phenomics research.To address the limitations of traditional manual methods—which are inefficient and destructive—and the poor flexibility of existing image-based methods that rely on manual priors,a graph structure-guided approach for skeleton parsing and non-destructive measurement of key phenotypic parameters was proposed.Firstly,building on the YOLO v9 framework,a more robust key point detection model was trained by incorporating mixed-background data augmentation and a Wise-IoU(WIoU)loss function to improve the detection of panicle nodes and neck nodes.Next,threshold segmentation and thinning were applied to panicle images to extract skeletal structures and construct an undirected graph topology.The detected key points were then deeply integrated with the skeleton topology to classify key point types and assist graph-based algorithms in automatically identifying the rachis,primary branches,and secondary branches.Physical scale conversion was achieved by using a calibration object.Experimental results demonstrated that the improved detection model increased mAP for neck nodes and panicle nodes by 4.5 and 2.4 percentage points,respectively,compared with the baseline,while recall was improved by 7.8 and 4.0 percentage points,and the correct detection ratio of key points was increased by 4.6 and 5.0 percentage points.In structural counting,panicle node counting achieved zero error,and the mean relative errors for primary and secondary branch counts were within 0.39%and 2.38%,respectively.For dimensional measurements,the mean relative errors of primary branch length,secondary branch length,rachis length,and internode length were controlled within 3.2%,7.5%,3.1%,and 5.2%,respectively,with mean absolute errors not exceeding 2.9 mm,2.3 mm,2.3 mm,and 1.6 mm.The research result achieved automatic and non-destructive extraction of key rice panicle phenotypic parameters,providing a viable technical solution for high-throughput panicle phenotyping.关键词
水稻/稻穗/表型参数/图结构引导/目标检测Key words
rice/rice panicle/phenotypic parameter/graph structure guidance/target detection分类
农业科技引用本文复制引用
ZHOU Yuncheng,LI Ruiyang,ZHANG Yu,LIANG Chengwei,WANG Jue..基于图结构引导的稻穗骨架解析与关键表型参数无损测量方法[J].农业机械学报,2026,57(1):92-103,12.基金项目
国家重点研发计划项目(2023YFD1501303、2021YFD1500204) （2023YFD1501303、2021YFD1500204）
