Rapid detection of the viability of naturally aged maize seeds using multimodal data fusion and explainable deep learning techniques

Abstract

Seed viability, a key indicator for quality assessment, directly impacts the emergence of field seedlings. The existing nondestructive testing model for maize seed vitality based on naturally aged seeds and predominantly relying on single-modal data like MV and RS, achieves an accuracy of less than 70 %. To elucidate the influence of different data on model accuracy, this study proposes the MSCNSVN model for detecting seed viability by collecting multisensor information from maize seeds using sensors, such as MV, RS, TS, FS, and SS. Our findings indicated that (1) the single-modal FS dataset achieved optimal prediction accuracy, with FS570/600 contributing the most; (2) multimodal data fusion outperformed single-modal data, with an accuracy improvement of 10 %, while the MV + RS + FS dataset achieved the highest accuracy; (3) the MSCNSVN model demonstrated superior performance compared to baseline models; (4) modeling with dual-variety datasets and endosperm surface datasets improved accuracy by 2 %–3 %.