Abstract: In response to the issue of low detection accuracy for mild moldy-core disease in apples using single methods, this study proposed an approach based on the fusion of near-infrared transmission spectroscopy and acoustic-vibration technology to enhance the discriminability of mild moldy-core disease in apples. For the near-infrared spectral signals, the impacts of different preprocessing and feature extraction methods on modeling outcomes were analyzed to select the spectral feature bands. For the acoustic-vibration signals, 7 time-domain features were optimized by using the YSV engineering test and signal analysis software as well as calculating Pearson correlation coefficients. The spectral feature bands and time-domain features were then concatenated to form a fused feature vector. Convolutional neural networks (CNN), long short-term memory (LSTM), and CNN-LSTM were employed to construct discrimination models based on single and fused features, separately. The performance analysis of the models revealed that the CNN-LSTM combination model, which integrated 15 near-infrared transmission spectral bands and 7 time-domain features, exhibited the best performance in discriminating mild moldy-core disease, with accuracy, recall, specificity, and F1 scores of 98.31%, 97.06%, 97.06%, and 97.90% on the test set, respectively. These findings demonstrate that the proposed method can effectively improve the discrimination accuracy of mild moldy-core disease in apples.

Key words: visible-near-infrared transmission spectroscopy; acoustic-vibration signals; apple moldy-core disease; feature fusion; convolutional neural networks-long short-term memory