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Maize seed variety identification using hyperspectral imaging and self-supervised learning: A two-stage training approach without spectral preprocessing

文献类型： 外文期刊

作者： Zhang, Liu 1 ; Zhang, Shubin 1 ; Liu, Jincun 1 ; Wei, Yaoguang 1 ; An, Dong 1 ; Wu, Jianwei 5 ;

作者机构： 1.China Agr Univ, Natl Innovat Ctr Digital Fishery, Beijing 100083, Peoples R China

2.China Agr Univ, Key Lab Smart Farming Technol Aquat Anim & Livesto, Minist Agr & Rural Affairs, Beijing 100083, Peoples R China

3.Beijing Engn & Technol Res Ctr Internet Things Agr, Beijing 100083, Peoples R China

4.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

5.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China

6.Beijing PAIDE Sci & Technol Dev Co Ltd, Beijing 100097, Peoples R China

关键词： Seed classification; Hyperspectral imaging; Self-supervised learning; Deep learning; Spectral analysis

期刊名称：EXPERT SYSTEMS WITH APPLICATIONS （ 影响因子：8.5； 五年影响因子：8.3 ）

ISSN： 0957-4174

年卷期： 2024 年 238 卷

页码：

收录情况： SCI

摘要： Rapid and non-destructive variety identification is essential for screening maize seeds for different end-uses such as food, feed, and breeding. Hyperspectral imaging (HSI) is one of the most commonly used techniques in such seed classification. Typically, after acquiring hyperspectral images of seeds, the spectral domain signals need to be preprocessed and a classifier need to be designed. The traditional method is to find a appropriate spectral preprocessing method through trial-and-error experiment, which is time-consuming, laborious and has high risk of misuse preprocessing. In view of this, this paper proposes a self-supervised learning method that includes pretraining and fine-tuning phases. In the pre-training phase, a model was trained on the unlabeled raw spectral data in an unsupervised manner to obtain general representations. In the fine-tuning phase, the pre-trained model was fine-tuned with the goal of the seed classification task and trained in a supervised manner on labeled spectral data. Experimental results showed that the proposed method did not rely on spectral preprocessing, and its performance was superior to other existing seed classification methods. In addition, the selfsupervised pre-trained model significantly outperformed the non-pre-trained model in the downstream seed classification task, and obtained good generalization ability. Overall, this method combined with HSI for seed quality evaluation has broad application prospects.

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