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Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

文献类型： 外文期刊

作者： Wu, Na 1 ; Liu, Fei 1 ; Meng, Fanjia 2 ; Li, Mu 3 ; Zhang, Chu 4 ; He, Yong 1 ;

作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou, Peoples R China

2.China Agr Univ, Coll Informat & Elect Engn, Beijing, Peoples R China

3.Jilin Acad Agr Sci, Maize Res Inst, Gongzhuling, Peoples R China

4.Huzhou Univ, Sch Informat Engn, Huzhou, Peoples R China

关键词： crop seeds; hyperspectral imaging; classification model; spectroscopic analysis; deep learning

期刊名称：FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY （ 影响因子：5.89； 五年影响因子：5.973 ）

ISSN： 2296-4185

年卷期： 2021 年 9 卷

页码：

收录情况： SCI

摘要： Rapid varieties classification of crop seeds is significant for breeders to screen out seeds with specific traits and market regulators to detect seed purity. However, collecting high-quality, large-scale samples takes high costs in some cases, making it difficult to build an accurate classification model. This study aimed to explore a rapid and accurate method for varieties classification of different crop seeds under the sample-limited condition based on hyperspectral imaging (HSI) and deep transfer learning. Three deep neural networks with typical structures were designed based on a sample-rich Pea dataset. Obtained the highest accuracy of 99.57%, VGG-MODEL was transferred to classify four target datasets (rice, oat, wheat, and cotton) with limited samples. Accuracies of the deep transferred model achieved 95, 99, 80.8, and 83.86% on the four datasets, respectively. Using training sets with different sizes, the deep transferred model could always obtain higher performance than other traditional methods. The visualization of the deep features and classification results confirmed the portability of the shared features of seed spectra, providing an interpreted method for rapid and accurate varieties classification of crop seeds. The overall results showed great superiority of HSI combined with deep transfer learning for seed detection under sample-limited condition. This study provided a new idea for facilitating a crop germplasm screening process under the scenario of sample scarcity and the detection of other qualities of crop seeds under sample-limited condition based on HSI.

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