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Hyperspectal imaging technology for phenotyping iron and boron deficiency in Brassica napus under greenhouse conditions

文献类型： 外文期刊

作者： Li, Hui 1 ; Wan, Long 1 ; Li, Chengsong 1 ; Wang, Lihong 1 ; Zhu, Shiping 1 ; Chen, Xinping 5 ; Wang, Pei 1 ;

作者机构： 1.Southwest Univ, Coll Engn & Technol, Key Lab Agr Equipment Hilly & Mt Areas, Chongqing, Peoples R China

2.Jiangsu Univ, Sch Agr Engn, Key Lab Modern Agr Equipment & Technol, Zhenjiang, Peoples R China

3.Chinese Acad Agr Sci, Natl Citrus Engn Res Ctr, Chongqing, Peoples R China

4.Southwest Univ, Chongqing, Peoples R China

5.Southwest Univ, Coll Resources & Environm, Interdisciplinary Res Ctr Agr Green Dev Yangtze Ri, Chongqing, Peoples R China

6.Chinese Acad Agr Sci, Inst Urban Agr, Chengdu, Peoples R China

关键词： deficiency identification; rapeseed; iron and boron; hyperspectral imaging; MSC-CARS-LDA

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.6； 五年影响因子：6.8 ）

ISSN： 1664-462X

年卷期： 2024 年 15 卷

页码：

收录情况： SCI

摘要： Introduction: The micronutrient deficiency of iron and boron is a common issue affecting the growth of rapeseed (Brassica napus). In this study, a non-destructive diagnosis method for iron and boron deficiency in Brassica napus (genotype: Zhongshuang 11) using hyperspectral imaging technology was established. Methods: The recognition accuracy was compared using the Fisher Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) recognition models. Recognition results showed that Multiple Scattering Correction (MSC) could be applied for the full band hyperspectral data processing, while the LDA models presented better performance on establishing the leaf iron and boron deficiency symptom recognition than the SVM models. Results: The recognition accuracy of the training set reached 96.67%, and the recognition rate of the prediction set could be 91.67%. To improve the model accuracy, the Competitive Adaptive Reweighted Sampling algorithm (CARS) was added to construct the MSC-CARS-LDA model. 33 featured wavelengths were selected via CARS. The recognition accuracy of the MSC-CARS-LDA training set was 100%, while the recognition accuracy of the MSC-CARS-LDA prediction set was 95.00%. Discussion: This study indicates that, it is capable to identify the iron and boron deficiency in rapeseed using hyperspectral imaging technology.

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