您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network

文献类型： 外文期刊

作者： Li, Yinglun 1 ; Wen, Weiliang 2 ; Guo, Xinyu 2 ; Yu, Zetao 3 ; Gu, Shenghao 2 ; Yan, Haipeng 4 ; Zhao, Chunjiang 1 ;

作者机构： 1.Jilin Agr Univ, Coll Resources & Environm, Changchun, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing Key Lab Digital Plant, Beijing, Peoples R China

4.Beijing Shunxin Agr Sci & Technol Co Ltd, Beijing, Peoples R China

期刊名称：PLOS ONE （ 影响因子：3.24； 五年影响因子：3.788 ）

ISSN： 1932-6203

年卷期： 2021 年 16 卷 1 期

页码：

收录情况： SCI

摘要： Image processing technologies are available for high-throughput acquisition and analysis of phenotypes for crop populations, which is of great significance for crop growth monitoring, evaluation of seedling condition, and cultivation management. However, existing methods rely on empirical segmentation thresholds, thus can have insufficient accuracy of extracted phenotypes. Taking maize as an example crop, we propose a phenotype extraction approach from top-view images at the seedling stage. An end-to-end segmentation network, named PlantU-net, which uses a small amount of training data, was explored to realize automatic segmentation of top-view images of a maize population at the seedling stage. Morphological and color related phenotypes were automatic extracted, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle. The results show that the approach can segment the shoots at the seedling stage from top-view images, obtained either from the UAV or tractor-based high-throughput phenotyping platform. The average segmentation accuracy, recall rate, and F1 score are 0.96, 0.98, and 0.97, respectively. The extracted phenotypes, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle, are highly correlated with manual measurements (R-2 = 0.96-0.99). This approach requires less training data and thus has better expansibility. It provides practical means for high-throughput phenotyping analysis of early growth stage crop populations.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台