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Data fusion-driven hyperspectral imaging for non-destructive detection of single maize seed vigor

文献类型： 外文期刊

作者： Shi, Rui 1 ; Zhang, Han 1 ; Wang, Cheng 1 ; Zhou, Yanan 1 ; Kang, Kai 1 ; Luo, Bin 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing, Peoples R China

2.Jiangsu Univ, Sch Agr Engn, Zhenjiang, Peoples R China

关键词： Hyperspectral imaging; Maize seed; Vigor detection; Single; Data fusion

期刊名称：MEASUREMENT （ 影响因子：5.6； 五年影响因子：5.4 ）

ISSN： 0263-2241

年卷期： 2025 年 253 卷

页码：

收录情况： SCI

摘要： Maize is one of the most significant food crops in the world, and the vigor of maize seeds is a crucial indicator of seed quality. Therefore, it is of paramount importance to accurately and non-destructively detect the vigor of single maize seeds. In this study, hyperspectral images of the endosperm side and embryo side of a single maize seed were collected, and the feasibility of using hyperspectral imaging for vigor detection of single maize seeds was investigated. Due to the differences between the two sides of the maize seed spectra, four data fusion proposals (Mean, Concat, Stack, and Parallel) were designed and utilized to establish single maize seed vigor detection models in order to effectively utilize the spectral data from both sides of the maize seed. Meanwhile, feature engineering methods were used to assist modeling. The findings indicate that data fusion exhibits a superior capacity for the detection of single maize seed vigor when compared to using single-side spectral data. Additionally, the convolutional neural network demonstrates remarkable capabilities in feature extraction and resilience to noise. Feature engineering can further enhance the model performance. Suitable preprocessing algorithms can be used to reduce noise in the original spectra and alleviate the problem of noise amplification by the data fusion approaches. Characteristic wavelength extraction can eliminate redundant information in the original spectra and reduce the parameters of the models. The experiment results demonstrated that the fusion of spectra from both sides of the seed can be successfully used for single maize seed vigor detection and provided a potential method for accurate quality detection of seeds.

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