Prediction of Vigor of Naturally Aged Seeds from Xishuangbanna Cucumber (Cucumis sativus L. var. xishuangbannanesis) Using Hyperspectral Imaging

文献类型： 外文期刊

第一作者： Zhang, Meng

作者： Zhang, Meng;Song, Jiangping;Jia, Huixia;Zhang, Xiaohui;Yang, Wenlong;Wang, Yang;Wang, Haiping;Zhang, Meng

作者机构：

关键词： Xishuangbanna cucumber; seed vigor; germplasm conservation; machine learning; hyperspectral imaging

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 10 期

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收录情况： SCI

摘要： Xishuangbanna cucumber (Cucumis sativus L. var. xishuangbannanesis), as a rare and endangered cucumber germplasm resource, possesses certain irreplaceable characteristics that make it difficult to reacquire once lost. To ensure long-term preservation of this germplasm, immediate propagation and regeneration are required after successful collection. Current germplasm management relying on conventional viability testing methods often leads to seed loss. Therefore, there is an urgent need to develop a rapid and non-destructive testing technology for assessing the seed viability of Xishuangbanna cucumber. This study integrated hyperspectral imaging technology with various data preprocessing methods, feature wavelength selection algorithms, and classification models to achieve rapid and non-destructive detection of Xishuangbanna cucumber seed viability. Hyperspectral imaging was employed to acquire spectral data from the seeds. Preprocessing methods including MSC (Multivariate Scattering Correction), SNV (Standard Normal Variety), FD (First Derivative), SD (Second Derivative), and L2NN (L2 Norm Normalization) were applied to enhance spectral data quality. Feature selection algorithms such as UVE (Uninformative Variables Elimination), SPA (Successive Projections Algorithm), and CARS (Competitive Adaptive Reweighted Sampling) were utilized to identify optimal spectral bands. Combined with KNN (K-Nearest Neighbor) and LogitBoost algorithms, predictive models for seed viability were established. The results demonstrated that the L2NN-KNN model outperformed other models, achieving an accuracy of 83.33%, precision of 86.99%, and an F1-score of 0.83. This study confirms that hyperspectral imaging combined with machine learning can effectively predict the viability of Xishuangbanna cucumber seeds, providing a novel technical approach for the conservation of rare and endangered cucumber germplasm resources. The findings hold significant implications for promoting long-term preservation and sustainable utilization of this valuable genetic material.

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