A maturity classification model for winter jujubes based on DSAF-ResNet

Abstract

Accurate, non-destructive classification of winter jujube maturity is critical for quality control and intelligent harvesting. This study proposes a dual-stream attention-fused residual network (DSAF-ResNet) combining hyperspectral and GLCM-based texture features at the feature level. The multimodal fusion significantly improved classification performance, with ResNet34 achieving 92.27% test accuracy under fused inputs. The DSAF-ResNet, integrating RepVGGBlock, SimAM attention, and a dual-stream architecture, achieved 98.61% training accuracy and 97.24% test accuracy, with 97.31% precision and 97.24% recall. Ablation experiments confirmed the contribution of each module. DSAF-ResNet demonstrated excellent generalization, stability, and robustness in distinguishing subtle maturity differences, even under class imbalance. This work provides an effective, scalable framework for non-destructive fruit maturity classification, advancing intelligent agricultural practices and supporting precision agriculture applications.