Online detection of apple moldy core using near-infrared spectroscopy with flexible transmission tray and deep learning.

Apple moldy core (AMC) causes substantial postharvest losses, yet early-stage infections remain difficult to detect due to the absence of visible symptoms. This study proposed an integrated, industry-ready approach that combines transmission near-infrared (NIR) spectroscopy with a custom flexible transmission tray and deep-learning classification to enable accurate, high-throughput detection of early AMC. The tray was engineered to stabilize fruit positioning, reduce ambient-light interference, and guide NIR illumination through the fruit core, yielding reproducible transmission spectra. Spectral data were preprocessed with Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, and mean centering. The study systematically evaluated wavelength selection strategies (CARS, SCARS and SCARS combined with SPA) and developed two-class (healthy/diseased) and three-class (healthy/mild/severe) classifiers using BP, CNN, LSTM and a hybrid CNN-LSTM architecture. The CNN-LSTM model trained on SCARS-SPA-selected wavelengths achieved the best performance, with classification accuracies of 98.82% (two-class) and 97.65% (three-class). These results demonstrate that the SCARS-SPA + CNN-LSTM pipeline, together with the flexible transmission tray, provides a robust and reproducible framework for early, precise AMC detection. The proposed system is compatible with conveyor-based integration and real-time sorting, offering a practical solution to reduce economic losses and improve quality control in commercial apple supply chains.