Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning

文献类型： 外文期刊

第一作者： Nie, Pengcheng

作者： Nie, Pengcheng;Zhang, Jinnuo;Feng, Xuping;He, Yong;Yu, Chenliang

作者机构：

关键词： Near-infrared hyperspectral imaging technology; Deep convolutional neural network; Hybrid seeds; t-Distribution stochastic neighbor embedding

期刊名称：SENSORS AND ACTUATORS B-CHEMICAL （ 影响因子：7.46； 五年影响因子：6.743 ）

ISSN： 0925-4005

年卷期： 2019 年 296 卷

页码：

收录情况： SCI

摘要： The rapid and efficient selection of eligible hybrid progeny is an important step in cross breeding. However, selecting hybrid offspring that meets specific requirements can be time consuming and expensive. Here, near-infrared hyperspectral imaging technology combined with deep learning was applied to classifying hybrid seeds. The hyperspectral images in the range of 975-1648 nm of a total of 6136 hybrid okra seeds and 4128 hybrid loofah seeds, which both contained six varieties, were collected. A partial least squares discriminant analysis, support vector machine and deep convolutional neural network (DCNN) were used to establish discriminant analysis models, and their performances were compared among the different hybrid seed varieties. The discriminant analysis model based on the DCNN was the most stable and had the highest classification accuracy, greater than 95%. The values of features in the last layer of the DCNN were visualized using t-distribution stochastic neighbor embedding. The discriminant analysis model based on the DCNN had the advantages of reducing the labor burden and time required in cross breeding-based progeny selection, which will accelerate the progress of related research.

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