Heat damage caused by elevated temperature and humidity during storage significantly affects soybean seed quality and viability. Early detection remains challenging due to the lack of visible symptoms in mildly damaged seeds. In this study, hyperspectral imaging (HSI) was adopted to capture detailed spectral information associated with internal physiological changes in soybean seeds. To simulate realistic thermal stress scenarios, soybean seeds were subjected to two temperature conditions: a control group stored at 25 °C and 55% RH and a heat-treated group stored at 45 °C and 80% RH. Within the high-temperature group, different durations (20 and 30 days) were used to generate mildly and severely damaged seeds, respectively. After image acquisition using a 400–1000 nm VNIR-HSI system, multiplicative scatter correction (MSC) was selected as the optimal preprocessing method to reduce scattering effects. Spectral dimensionality was then reduced using a recursive feature elimination–principal component analysis (RFE-PCA) cascade to retain key discriminative features. Finally, a support vector classifier was constructed and optimized using a Lévy–Sine-enhanced Egret Swarm Optimization Algorithm (LSESOA), yielding a classification accuracy of 96.75% and a macro-F1 score of 96.76% on the test set. This study demonstrates the feasibility of applying HSI combined with metaheuristic optimization to achieve accurate, non-destructive evaluation of heat-damaged soybean seeds, providing technical support for quality control during storage and logistics.
Tan et al. (Mon,) studied this question.