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

A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

文献类型： 外文期刊

作者： Tu, Keling 1 ; Wen, Shaozhe 2 ; Cheng, Ying 1 ; Xu, Yanan 1 ; Pan, Tong 1 ; Hou, Haonan 1 ; Gu, Riliang 1 ; Wang, Jianhua 1 ; Wang, Fengge 3 ; Sun, Qun 1 ;

作者机构： 1.China Agr Univ, Innovat Ctr Beijing Crop Seeds Whole Proc Technol, Beijing Key Lab Crop Genet Improvement,Dept Plant, Minist Agr & Rural Affairs,Coll Agron & Biotechno, Beijing 100193, Peoples R China

2.Beijing Acad Agr & Forestry Sci BAAFS, Beijing Vegetable Res Ctr, Beijing Key Lab Vegetable Germplasm Improvement, Beijing 100097, Peoples R China

3.Beijing Acad Agr & Forestry Sci BAAFS, Maize Res Ctr, Beijing Key Lab Maize DNA Fingerprinting & Mol Br, Beijing 100097, Peoples R China

关键词： Maize seed; High-throughput; Phenotyping; Non-destructive testing; Varietal purity

期刊名称：PLANT METHODS （ 影响因子：5.827； 五年影响因子：5.904 ）

ISSN：

年卷期： 2022 年 18 卷 1 期

页码：

收录情况： SCI

摘要： Background: Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties. Specifically, we obtained image data of seed morphology and hyperspectral reflectance for Jingke 968 and nine other closely-related varieties (non-Jingke 968). We then compared the robustness of three common machine learning algorithms in distinguishing these varieties based on the phenotypic imaging data. Results: Our results showed that hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy, while machine vision imaging provided similar to 90% accuracy. Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties. Conclusions: This strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops.

  • 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