Nondestructive discrimination of moldy pear core based on the recurrence plots of vibration acoustic signals and deep convolutional neural networks

Moldy core is a serious internal defect in pears. Since there is no significant difference in appearance between the healthy pears and those with mild moldy core, it is still a great challenge for the early detection of moldy pear core. This study transformed the vibration acoustic signals (VA signal) of pears into recurrence plots and Markov transition field to enable image-based classification of moldy cores. In addition to traditional machine-learning baselines (Random Forest and k-Nearest Neighbors) trained on LBP-extracted texture features from RP/MTF, the deep models were constructed and compared, which include ResNet-101, DenseNet-121, SqueezeNet, Vision Transformer (ViT), and an improved SqueezeNet (ISqueezeNet). Hyperparameters were tuned via Bayesian optimization over optimizer type, learning rate, batch size, and L2 weight decay, yielding model-specific optimal settings. Under these configurations, the ISqueezeNet achieved the highest test accuracy of 93.05%, with class-wise accuracies of 89.28% (healthy), 96.15% (slight), and 94.44% (moderate and severe). Comparisons with lightweight networks (MobileNetV1 and ShuffleNetV2) further showed that ISqueezeNet attains superior accuracy with favorable parameter efficiency and inference speed. Grad-CAM visualizations confirmed that the model focuses on lesion-relevant regions, supporting interpretability and practical reliability. These results indicate that the proposed approach is promising for early, nondestructive detection of moldy pear cores.