Detection of apple moldy core disease by fusing vibration and Vis/NIR spectroscopy data with dual-input MLP-Transformer

Abstract

Moldy core is a highly contagious internal disease of apples, and even a small number of diseased apples can trigger large-scale infections during the storage stage. In this study, a combined acoustic vibration and Vis/NIR spectroscopy method for moldy core apple identification was proposed to improve the accuracy of moldy core identification. The vibration signals and Vis/NIR spectroscopy of apples were collected using a self-designed micro-LDV detection device and Vis/NIR spectroscopy online detection device respectively for constructing multiple moldy core apple classification models. The results showed that the classification model combining vibration spectrum and Vis/NIR spectral data had significant advantages in apple moldy core identification accuracy compared to the classification model using a single vibration spectrum or Vis/NIR spectral data. Ultimately, dual-input MLP-Transformer (DMLPT) demonstrated the best recognition performance with an overall classification accuracy of 99.31% for the model, with 100%, 97.56%, 100.00% and 100% accuracy for normal, mild moldy core disease, moderate moldy core disease and severe moldy core disease apples, respectively. This study demonstrated the excellent performance and great potential of acoustic vibration and visible/near-infrared spectral data fusion for fruit internal quality detection.