Rapid Seed Viability Detection Using Laser Speckle Weighted Generalized Difference with Improved Residual Networks

Conventional seed viability assessment methods are often destructive, time-consuming, and highly sensitive to environmental conditions, resulting in estimated annual global agricultural losses exceeding 12 billion USD, as reported by the Food and Agriculture Organization (FAO) of the United Nations. To overcome these limitations, this study proposes a non-destructive framework for evaluating the viability of multiple pea seed varieties—including Gancui-2, Jinwan No.6, Hongyun 211, Mawan No.1, and Wuxuwan No.2—using laser speckle imaging (LSI). A He–Ne laser combined with a CCD camera was employed to capture 512-frame dynamic speckle sequences from 3000 seeds. A weighted generalized difference (WGD) algorithm was developed to enhance feature extraction by emphasizing physiologically relevant temporal variations through frame weighting based on the global mean and standard deviation of inter-frame differences. The extracted features were classified using an improved Weighted Generalized Residual Network (ResNet-W), which integrates weighted average pooling and 1 × 1 convolution to enhance feature aggregation and classification efficiency. Experimental results demonstrated strong performance, achieving 91.32% accuracy, 90.78% precision, 92.04% recall, and a 91.38% F1-score. The proposed framework offers a cost-effective, high-accuracy, and fully non-destructive solution for seed viability assessment, with significant potential for real-time agricultural quality monitoring and intelligent seed sorting applications.