Abstract: In this study, a nondestructive vibro-acoustic setup was employed to acquire the vibro-acoustic signals of pear fruit. The signals were decomposed using the empirical mode decomposition (EMD). Different methods were used to suppress the end effect and mode mixing to achieve the optimal signal decomposition components. Then, the decomposition components of the vibro-acoustic signals were used as the input to construct a discriminant model based on convolution neural networks with spatial pyramid pool (CNN-SPP). The results showed that the improved slope-based method was better able to suppress the EMD end effect for the vibro-acoustic signals. The complementary complete ensemble empirical mode decomposition with adaptive noise (CCEEMDAN) method could exhibit better performance for suppressing mode mixing after end effect suppression. Thus, the obtained components were used as the input to construct a CCEEMDAN-CNN-SPP-based discriminant model. The overall classification accuracy of the model was 93.66% for pears with core browning, the discrimination accuracy was 94.44% for sub-healthy pears, and the misjudgment rate was 6.35% for diseased fruit. This improved the accuracy of vibro-acoustic identification of pears with early-stage mild disease. This study lays a foundation for the development of an online detection system for sub-healthy fruits in the future.

Key words: core browning of pears; nondestructive detection; vibro-acoustic method; empirical mode decomposition; convolutional neural network