Acoustic vibration multi-domain images vision transformer (AVMDI-ViT) to the detection of moldy apple core: Using a novel device based on micro-LDV and resonance speaker

Moldy-core is a common internal disease in apples, and apples infected with this disease cannot be directly identified according to their external characteristics. In this study, a novel acoustic vibration device based on micro-LDV, resonance speaker and microphone was employed to detect moldy-core in apples, the acoustic vibration signals of healthy apples and apples with different degrees of moldy-core were converted into acoustic vibration multi-domain images (AVMDI), which consisted of time-frequency images generated through continuous wavelet transform (CWT), as well as time-domain and frequency-domain images generated through Gramian Angular Field (GAF). Subsequently the combination of AVMDI and Vision Transformer (ViT) was applied to the identification of internal defects in fruits. The outcomes evince that the classification efficacy of the model, amalgamating sound and vibration signals, surpasses that of models reliant on solitary sound or vibration signals. The AVMDI-ViT model achieved an overall classification accuracy of 97.96 %. Specifically, it achieved 100 % accuracy in identifying healthy apples, 94.74 % accuracy in identifying mild moldy-core apples (<= 7 %), 97.50 % accuracy in identifying moderate moldy-core apples (> 7 % and <= 15 %), and 100 % accuracy in identifying severe moldy-core apples (> 15 %). The proposed method demonstrates a high level of accuracy in the identification of moldy-core apples, while also offering advantages in terms of simplicity, speed, cost-effectiveness and non-contact detection.