农业机械学报2026，Vol.57Issue(1)：62-71,10.DOI:10.6041/j.issn.1000-1298.2026.01.006
基于YOLO和增强现实的玉米穗位高实时测量方法
Real-time Measurement of Maize Ear Height Based on YOLO and Augmented Reality
摘要
Abstract
Efficient and accurate monitoring of maize ear height(EH)is critical for anti-lodging breeding.The traditional manual measurement approach is labor-intensive and time-consuming,while existing automated approaches often lack robustness under varying field conditions or involve high costs.To address these limitations,an iOS application(APP)was developed based on the you only look once(YOLO)model and augmented reality(AR)technology for real-time,accurate,efficient,and low-cost maize EH measurement.It comprised two modules:a maize ear detection model and a height measurement module.The ear detection model was trained and validated on a dataset comprising 1 000 field images collected from maize fields during the filling stage,under various lighting and occlusion conditions.Among different object detection models,the YOLO v5s model demonstrated the most robust performance with a precision of 0.844,a recall of 0.724,and an AP0.5 of 0.814.The trained detection model had been integrated into a maize EH measurement system,which utilized the AR technology for real-time measurement.It demonstrated excellent compatibility and performance on iOS devices,with response time below 0.3 s.Field evaluation results indicated a high correlation between the EH measured by the app and manual measurements(R2=0.750～0.864,RMSE=0.10～0.13m).Theappwas optimized for solo operation.To finish measuring a plot with over 10 maize plants only took less than 2 minutes,which was over 6 times faster than that of the traditional measurement with the leveling rod.This app significantly improved the efficiency of maize EH measurements while maintaining accuracy,providing real-time and precise data support for field management and breeding programs.关键词
玉米雌穗检测/深度学习/增强现实/穗位高测量/YOLO v5sKey words
maize ear detection/deep learning/augmented reality/ear height measurement/YOLO v5s分类
信息技术与安全科学引用本文复制引用
ZHANG Yaling,LIU Yadong,LI Liming,YU Xun,NAN Fei,YIN Dameng,JIN Xiuliang..基于YOLO和增强现实的玉米穗位高实时测量方法[J].农业机械学报,2026,57(1):62-71,10.基金项目
新一代人工智能国家科技重大专项(2022ZD0115701)、国家自然科学基金项目(42071426、42301427)、中国农业科学院南繁专项(PTXM2501、PTXM2402)和中国农业科学院科技创新工程项目 （2022ZD0115701）
