An interpretable nondestructive detection model for maize seed viability: Based on grouped hyperspectral image fusion and key biochemical indicators

Abstract

Seed viability is crucial for ensuring crop quality and yield. However, existing nondestructive detection methods, which primarily rely on spectroscopic techniques and simple data fusion strategies, often suffer from limited accuracy and reliability. To address these limitations, this study proposes a novel, highly accurate, and interpretable nondestructive approach for evaluating maize seed viability. With regard to enhancing the prediction accuracy of seed viability, a grouped hyperspectral image fusion (GHIF) strategy was proposed to more effectively integrate complementary information from visible-near-infrared hyperspectral imaging (VisNIR-HSI) and fluorescence hyperspectral imaging (Fluo-HSI) datasets. With respect to improving model interpretability, eight biochemical components in the embryo of maize seeds were measured, and two key biochemical indicators—catalase (CAT) activity and malondialdehyde (MDA) content—were identified and validated as highly correlated with seed viability and predictable from spectral data. Building on these findings, a two-stage detection model was constructed. In the first stage, the two key biochemical indicators were predicted from the fused data using regression models. In the second stage, seed viability was determined using a dual-threshold strategy based on the predicted biochemical values. Experimental results showed that the proposed method achieved 90 % classification accuracy, comparable to direct spectral models while offering greater interpretability. This approach provides a reliable and explainable solution for nondestructive seed viability evaluation.