您好，欢迎访问吉林省农业科学院 机构知识库！
吉林省农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Application of near-infrared hyperspectral imaging for variety identification of coated maize kernels with deep learning

文献类型： 外文期刊

作者： Zhang, Chu 1 ; Zhao, Yiying 1 ; Yan, Tianying 3 ; Bai, Xiulin 1 ; Xiao, Qinlin 1 ; Gao, Pan 3 ; Li, Mu 4 ; Huang, Wei 4 ; Ba 1 ;

作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Spect Sensing, Hangzhou 310058, Peoples R China

3.Shihezi Univ, Coll Informat Sci & Technol, Shihezi 832000, Peoples R China

4.Jilin Acad Agr Sci, Maize Res Inst, Gongzhuling 136100, Peoples R China

关键词： Deep learning; Coated maize kernels; Feature extraction; Feature fusion; Near-infrared hyperspectral imaging

期刊名称：INFRARED PHYSICS & TECHNOLOGY （ 影响因子：2.638； 五年影响因子：2.581 ）

ISSN： 1350-4495

年卷期： 2020 年 111 卷

页码：

收录情况： SCI

摘要： Tracing the varieties of seeds is of great importance for the seed industry. Maize kernels for planting are generally coated to protect kernels from fungi and insects. In this study, near-infrared hyperspectral imaging ranging from 874 nm to 1734 nm was used to identify the varieties of coated maize kernels. Spectral data were extracted and preprocessed. Logistic regression (LR) and support vector machine (SVM), convolutional neural network (CNN), recurrent neural network (RNN) and Long Short-Term Memory (LSTM) were used to build classification models. Furthermore, principal component analysis (PCA), CNN, RNN and LSTM were adopted to extract features. The extracted features were fused as the inputs of the classification models. Classification models using full spectra, extracted features and fused features obtained performances with the classification accuracy over 90% in the calibration, validation and prediction sets of most models. Models using extracted features obtained equivalently or slightly worse results than those using full spectra. The models using fused features all obtained good performances, with the classification accuracy over 90% in all sets. The overall results illustrated that near-infrared hyperspectral imaging with deep learning methods was a useful alternative for identifying coated maize varieties.

  • 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：吉林省农业科学院

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