Detection of sub-healthy apples with moldy core using deep-shallow learning for vibro-acoustic multi-domain features

Abstract

The apples with early moldy-core symptoms are in the sub-healthy status and still keep commodity value. The accurate recognition of sub-healthy fruit with moldy core is a valuable and challenging task for the apple industry. In this study, the well-trained IResNet50 network was used as a feature extractor to automatically mine the sensitive features from pixel grid images for time and frequency domains, and the time-frequency images. The extracted multi-domain deep features were fused by adaptive weighting and then fed into two shallow machine learning algorithms including support vector machine (SVM) and extreme learning machine (ELM) for classification. The parameters of SVM and ELM classifiers were optimized by a particle swarm optimization algorithm (PSO). The constructed IResNet50-PSO-ELM hybrid model achieved the overall classification accuracy of 96.7 %, and the recall value of 100 % for healthy fruit, 94.1 % for sub-healthy fruit, 96.2 % for diseased fruit. Also, the F1 values of the IResNet50-PSO-ELM hybrid model were over 95 % for the three-class classification. A comparison with existing studies demonstrated the potential of the proposed method for detecting the sub-healthy apples with moldy core.