Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning
文献类型： 外文期刊
作者： Nie, Pengcheng 1 ; Zhang, Jinnuo 1 ; Feng, Xuping 1 ; Yu, Chenliang 2 ; He, Yong 1 ;
作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, 866 Yuhangtang Rd, Hangzhou 310058, Zhejiang, Peoples R China
2.Zhejiang Acad Agr Sci, Vegetable Res Inst, Hangzhou 310021, Zhejiang, Peoples R China
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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