An end-to-end seed vigor prediction model for imbalanced samples using hyperspectral image
Tiantian Pang,
Chengcheng Chen,
Ronghao Fu
et al.
Abstract:Hyperspectral imaging is a key technology for non-destructive detection of seed vigor presently due to its capability to capture variations of optical properties in seeds. As the seed vigor data depends on the actual germination rate, it inevitably results in an imbalance between positive and negative samples. Additionally, hyperspectral image (HSI) suffers from feature redundancy and collinearity due to its inclusion of hundreds of wavelengths. It also creates a challenge to extract effective wavelength infor… Show more
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