Temperature Stress Detection Method of Rapeseed Seedling Based on Hyperspectral Imaging

被引：0
作者：
Zhang X. [1 ,2 ]
Zhang Y. [1 ,2 ]
Jiang H. [3 ]
Wang Y. [1 ,2 ]
Lin Y. [1 ,2 ]
Rao X. [1 ,2 ]
机构：
[1] College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou
[2] Key Laboratory of on Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou
[3] School of Mathematical Sciences, Zhejiang University, Hangzhou
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2021年 / 52卷 / 06期
关键词：
Feature fusion; Hyperspectral imaging; Rapeseed; Temperature stress;
D O I：
10.6041/j.issn.1000-1298.2021.06.024
中图分类号：
学科分类号：
摘要：
In order to ensure the quality of seedlings and provide healthy and robust seedlings to meet the needs of large-scale and standardization of modern rapeseed industry, a 21 d temperature stress experiment of rapeseed seedling was carried out. The aim was to study the identification of robust seedlings of rape under temperature stress using hyperspectral imaging technology. Firstly, the sensitive bands of temperature stress were extracted by spectral reflectance and continuous wavelet transform. And then the continuous projection algorithm and continuous wavelet transform-stepwise discriminant analysis were respectively used to extract characteristic wavelengths from sensitive bands of temperature stress. The waveband features and spectral features of rapeseed seedlings were analyzed with time. A total of seven features were selected, including the curve area at band MA and tangent eigenvalue tanθ of 554~714 nm, the reflectance value at 1 213 nm and 1 567 nm, wavelet feature w(9, 967), w(13, 1 213) and w(7, 1 567) to establish a multi-feature fusion Fisher discriminant model. The results showed that the average classification accuracy of the model was 88.68%, and the best detection accuracy reached 95.56% at the three-leaf stage, which could better distinguish the temperature stressed rape seedlings and provide a reference for the rapid detection of robust rape seedlings based on hyperspectral technology. © 2021, Chinese Society of Agricultural Machinery. All right reserved.
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页码：232 / 241and276
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