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

Evaluating Maize Grain Quality by Continuous Wavelet Analysis Under Normal and Lodging Circumstances

文献类型： 外文期刊

作者： Zhang, Jingcheng 1 ; Gu, Xiaohe 1 ; Wang, Jihua 1 ; Huang, Wenjiang 1 ; Dong, Yingying 1 ; Luo, Juhua 1 ; Yuan, Lin 1 ; L 1 ;

作者机构： 1.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Zhejiang Univ, Inst Agr Remote Sensing & Informat Syst App, Hangzhou 310029, Zhejiang, Peoples R China

3.China Rural Technol Dev Ctr, Gen Planning & Supervis Div, Beijing 100045, Peoples R China

关键词： Hyperspectral Measurement; Grain Quality; Continuous Wavelet Analysis; Partial Least Squares Regression

期刊名称：SENSOR LETTERS （ 影响因子：0.558； 五年影响因子：0.58 ）

ISSN： 1546-198X

年卷期： 2012 年 10 卷 1-2 期

页码：

收录情况： SCI

摘要： Root lodging is the most common stress that occurred in maize growing period. It has a great impact on both yield and grain quality. This study aims at developing some leaf level non-contact detecting models of grain quality for both maize plants under lodging and normal circumstances. In Anthesis and Spinning stages of maize growth, an artificial lodging was manipulated to simulate the naturally occurred physical force like windstorm. The hyperspectral measurements of three-ear-leaves were taken for both normal and lodged plants by the ASD FieldSpec Pro spectrometer. The contents of oil, protein and starch in the grain were measured by an automated near-infrared grain analyzer. A two-tailed t-test was used to identify the grain quality properties with significant difference between normal and lodging treatments. A continuous wavelet analysis (CWT) was employed to extract the spectral features of grain quality contents. Based on these spectral features, a partial least squares (PLS) regression was applied in developing the predicting models of grain quality parameters for both normal and lodging samples. As shown in the results, the wavelet transformed spectral features were successfully generated for both protein and starch samples, yet with varied wavelength positions and decomposition scales between normal and lodging treatments. The accuracy of the multiple regression models was relatively high, with an R-2 over 0.75 for all predicting models. The potential of CWT analysis in predicting maize grain quality parameters under both normal and lodging circumstances was thus illustrated.

  • 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