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Maturity determination of single maize seed by using near-infrared hyperspectral imaging coupled with comparative analysis of multiple classification models

文献类型： 外文期刊

作者： Wang, Zheli 1 ; Tian, Xi 1 ; Fan, Shuxiang 1 ; Zhang, Chi 1 ; Li, Jiangbo 1 ;

作者机构： 1.Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

2.Shihezi Univ, Coll Mech & Elect Engn, Shihezi 832003, Peoples R China

关键词： Hyperspectral imaging; Maize seed; Maturity; Wavelength selection

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： 2021 年 112 卷

页码：

收录情况： SCI

摘要： Maize is an important food crop in the world and it is used in many fields. The classification of maize seed maturity is of great value because it could increase the yield. In this study, near-infrared hyperspectral imaging (NIR-HSI) was employed to explore the maturity classification of maize seeds. In order to observe the influence of spectra of different positions in maize seed for modeling, the hyperspectral images of embryo and endosperm sides of maize seeds were collected in the spectral range of 1000-2300 nm. The average spectra of the embryo side (T1) and endosperm (T2) side were extracted from hyperspectral images, and then, the average spectra of both sides of maize seed (T3) were also calculated. T1, T2 and T3 spectra were used to build calibration models for maturity classification, respectively. And T1 and T2 spectra were imported into these developed classification models, and the classification accuracy of two types of spectra in the model was used to evaluate model applicability. These modeling methods including partial least square discriminant analysis (PLS-DA), decision tree (DT) and adaptive boosting (AdaBoost) methods. The principal component analysis (PCA) was applied to select feature wavelengths, common peaks and valleys in the loading curves of PC1 and PC2 were regarded as feature wavelengths. In order to reduce the influence of division of the calibration set, 50 randomized independent trials were carried out, and the average accuracy and stableness were used to evaluate the performance of models. Comparing among all models, PLS-DA model based on feature wavelengths selected by T2 spectra obtained the optimal results. When T1 and T2 were used as input to the optimal model, the classification accuracy was 98.7% and 100%, respectively. These results demonstrate the potential of the hyperspectral imaging technology for the rapid and accurate classification of maize seed maturity, and the feature wavelengths selected from the endosperm side combined with PLS-DA algorithm could establish a stable model.

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