主办单位：中国气象局沈阳大气环境研究所
国际刊号：ISSN 1673-503X
国内刊号：CN 21-1531/P
气象与环境学报

气象与环境学报 ›› 2022, Vol. 38 ›› Issue (2): 105-111.doi: 10.3969/j.issn.1673-503X.2022.02.013

• 快报 • 上一篇    

春玉米根系原位成像图片自动识别研究

贾庆宇1(),谢艳兵1,赵一俊2,王荣3,刘晶淼4,*(),温日红1   

  1. 1. 中国气象局沈阳大气环境研究所, 辽宁 沈阳 110166
    2. 盘锦市气象服务中心, 辽宁 盘锦 123010
    3. 北京力科惠泽科技有限公司, 北京 100010
    4. 中国气象科学研究院, 北京 100081
  • 收稿日期:2020-08-22 出版日期:2022-04-28 发布日期:2022-04-24
  • 通讯作者: 刘晶淼 E-mail:jiaqingyu@iaesy.cn;liujm@cma.gov.cn
  • 作者简介:贾庆宇, 男, 1978年生, 副研究员, 主要从事陆面过程与全球变化方面研究, E-mail: jiaqingyu@iaesy.cn
  • 基金资助:
    辽宁省“兴辽英才计划”(XLYC1807262);辽宁省气象局科研课题(BA202105);辽宁省气象局科研课题(BA202003);中国气象局沈阳大气环境研究所项目(2020SYIAEJY19)

Study on automatic recognition of root system image of spring maize in situ

Qing-yu JIA1(),Yan-bing XIE1,Yi-jun ZHAO2,Rong WANG3,Jing-miao LIU4,*(),Ri-hong WEN1   

  1. 1. Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China
    2. Panjin Meteorological Service Center, Panjin 123010, China
    3. Beijing Eco-mind Technology CO., LTD, Beijing 100010, China
    4. Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • Received:2020-08-22 Online:2022-04-28 Published:2022-04-24
  • Contact: Jing-miao LIU E-mail:jiaqingyu@iaesy.cn;liujm@cma.gov.cn

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摘要：

微根窗成像技术推动了植物根系表型研究，但是根系长度和直径仍然需要人眼识别再绘制轨迹，消耗大量的人力和时间。为了解决这一难题，本研究将U-Net语义分割技术引入到植物根系图像识别中，研发了基于机器学习的iRoot-V02根系自动识别软件。采用iRoot-V02软件识别微根窗法获得的植物根系成像图片的根长、直径、投影面积、根尖数等参数。结果表明: iRoot-V02软件批处理600 dpi图片的速度为每张26.6 S; 获取根系的骨架信息、总根长与人眼识别结果基本一致; 按直径每0.1 mm为一级，获得不同直径的根长，与人眼识别结果的根长决定系数大于0.76;精确捕捉到根系生长旺盛期不同直径根长的变化; 分析了300 dpi和600 dpi两种分辨率根系图片的参数，两种分辨率结果具有高相关性，因此可建立低分辨率根系参数和高分辨率根系参数之间的关系方程，采用低分辨率拍摄根系图像，通过方程转化成更准确的根系参数，减轻工作量。用iRoot-V02软件的根系生长信息近似于人眼识别，相比人眼识别在大批量根系图像智能识别、自动化、快速目标检测方面具有巨大优势。

关键词: 根系成像, 自动识别, iRoot-V02软件

Abstract:

Minirhizotron technology promotes the study of root phenotype, but the root length and diameter still require human eye recognition to draw the track, which consumes a lot of manpower and time.This study introduced U-Net semantic segmentation technology into plant root image recognition, and developed iRoot-V02 software based on machine learning to solve this problem.The iRoot-V02 software was used to identify the root length, diameter, projection area, and root tip number from plant root imaging images obtained by Minirhizotron technology.The results show that the average speed of iRoot-V02 software for processing 600 dpi images in batches is 26.6 seconds per picture.The skeleton information and total length of roots are essentially consistent with the human eye recognition results.According to the diameter of each 0.1 mm as a level, the correlation coefficient between the obtained root length of different diameters and the eye recognition results is larger than 0.76, reflecting that the software accurately captures the changes of root lengths of different diameters in the vigorous growth period of maize.In addition, the analysis of the parameters of root images with 300 dpi and 600 dpi resolutions shows that their results are highly correlated.Therefore, the relationship equation between low-and high-resolution root parameters can be established to confirm more accurate root parameters with the low-resolution root image and to reduce the workload.Root growth information obtained by using iRoot-V02 software is similar to that of human eye recognition.On a whole, compared with human eye recognition, iRoot-V02 has great advantages in mass root image intelligent recognition, automation and fast target detection.

Key words: Root image, Automatic identification, iRoot-V02 software

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