Early apple moldy core classification via multi-modal sensing and SE-ResNet18

Apple moldy core disease is a major pathogenic disease that severely degrades the postharvest quality of apples, and its early internal lesions cannot be directly identified through visual appearance observation. To realize efficient and non-destructive early diagnosis, this study proposes a multimodal image coding method fusing Visible-Near Infrared Spectroscopy (Vis-NIR) and Electronic Nose (E-nose) data, which is combined with the SE-ResNet18 deep learning model for disease classification. By virtue of coding techniques including Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence Plot (RP), one-dimensional time-series and spectral data were converted into image representations, so as to visualize their spatiotemporal patterns and enhance subtle disease-related features. On this basis, a two-branch SE-ResNet18 model based on the channel attention mechanism was constructed to improve the feature representation capability and achieve effective modal fusion. Experimental results show that the multimodal fusion model achieves a classification accuracy of 95.93%, which is significantly superior to single-modal methods, thus verifying the effectiveness of multi-source information complementarity. Ablation experiments further indicate that the SE attention module plays a crucial role in feature calibration and modal balance. Information entropy analysis reveals that the proposed method effectively enhances the discriminability of information during the feature extraction process. This study provides a solution with a clear theoretical basis and reliable performance for the non-destructive detection of early diseases in agricultural products, which has favorable application prospects and popularization potential.