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Study on the optimal algorithm prediction of corn leaf component information based on hyperspectral imaging

文献类型： 外文期刊

作者： Wu, Qiong 1 ; Wang, Jihua 2 ; Wang, Cheng 3 ; Xu, Tongyu 1 ;

作者机构： 1.Shenyang Agr Univ, Coll Informat & Electrificat Engn, Shenyang 110866, Peoples R China

2.Beijing Res Ctr Agri Food Testing & Farmland Moni, Beijing 100097, Peoples R China

3.Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

关键词： Hyperspectral imaging;PLS;Corn seeding;Chlorophyll content;GA

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： 2016 年 78 卷

页码：

收录情况： SCI

摘要： Genetic algorithm (GA) has a significant effect in the band optimization selection of Partial Least Squares (PLS) correction model. Application of genetic algorithm in selection of characteristic bands can achieve the optimal solution more rapidly, effectively improve measurement accuracy and reduce variables used for modeling. In this study, genetic algorithm as a module conducted band selection for the application of hyperspectral imaging in nondestructive testing of corn seedling leaves, and GA-PLS model was established. In addition, PLS quantitative model of full spectrum and experienced-spectrum region were established in order to suggest the feasibility of genetic algorithm optimizing wave bands, and model robustness was evaluated. There were 12 characteristic bands selected by genetic algorithm. With reflectance values of corn seedling component information at spectral characteristic wavelengths corresponding to 12 characteristic bands as variables, a model about SPAD values of corn leaves acquired was established by PLS, and modeling results showed r = 0.7825. The model results were better than those of PLS model established in full spectrum and experience-based selected bands. The results suggested that genetic algorithm can be used for data optimization and screening before establishing the corn seedling component information model by PLS method and effectively increase measurement accuracy and greatly reduce variables used for modeling. (C) 2016 Elsevier B.V. All rights reserved.

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