智慧农业（中英文）2025，Vol.7Issue(2)：95-105,11.DOI:10.12133/j.smartag.SA202501001
基于改进YOLOv11-Pose的玉米植株骨架及表型参数提取方法
Extraction Method of Maize Plant Skeleton and Phenotypic Parameters Based on Improved YOLOv11-Pose
摘要
Abstract
[Objective]Accurate extraction of maize plant skeletons and phenotypic parameters is fundamental for acquisition of plant growth data,morphological analysis,and agricultural management.However,leaf occlusion and complex backgrounds in dense plant-ing environments pose significant challenges to skeleton and parameters extraction.A maize plant skeleton and phenotypic parameters extraction method suitable for dense field environments was proposed in this research to enhance the extraction precision and efficien-cy,and provide technical support for maize growth data acquisition.[Methods]An improved YOLOv11-Pose multi-object keypoint de-tection network was introduced,a top-down detection framework was adopted to detect maize plant keypoints and reconstruct skele-tons.A uniform sampling algorithm was used to design a keypoint representation method tailored for maize skeletons and optimize task adaptability.Additionally,a single-head self-attention mechanism and a convolutional block attention module were incorporated to guide the model's focus on occluded regions and connected parts,thereby improve its adaptability to complex scenarios.[Results and Discussion]In dense field maize environments,experimental results showed that when the number of uniformly sampled key-points was set to 10,the Fréchet distance reached its minimum value of 79.008,effectively preserving the original skeleton's morpho-logical features while avoiding the negative impact of redundant points.Under this configuration,the improved YOLOv11-Pose mod-el achieved a bounding box detection precision of 0.717.The keypoint detection mAP50 and mAP50-95 improved by 10.9%and 23.8%,respectively,compared to the original model,with an inference time of 52.7 ms per image.The results demonstrated the mod-el's superior performance and low computational cost in complex field environments,particularly in keypoint detection tasks with en-hanced accuracy and robustness.The study further combined the results of skeleton extraction and spatial geometric information to achieve a plant height measurement mean average error(MAE)of 2.435 cm,the detection error of leaf age was less than one growth period,and the measurement error of leaf length was 3.482%,verifying the effectiveness and practicability of the proposed method in the application of phenotypic parameter measurement.[Conclusion]The proposed improved YOLOv11-Pose model can efficiently and accurately extract maize plant skeletons,meeting the demands of ground-based maize growth data acquisition.The research could pro-vide technical support for phenotypic data acquisition in grain production and precision agricultural management.关键词
作物长势/关键点检测/注意力机制/表型参数/玉米植株骨架/YOLOv11Key words
crop growth/keypoint detection/attention mechanism/phenotypic parameter/maize plant skeleton/YOLOv11分类
计算机与自动化引用本文复制引用
牛子昂,裘正军..基于改进YOLOv11-Pose的玉米植株骨架及表型参数提取方法[J].智慧农业（中英文）,2025,7(2):95-105,11.基金项目
国家重点研发计划项目(2023YFD2000101) National Key Research and Development Program of China(2023YFD2000101) （2023YFD2000101）
