Recognition of mango fruit diseases based on image processing and deep transfer learning

Objective  To realize non-destructive detection of the internal quality of mangoes and disease identification and classification based on the CT sequence images of mangoes obtained by computed tomography (CT) equipment.

Method  We used piecewise function method and median filter combined with bilateral filter to achieve image enhancement, used local adaptive threshold method to achieve binarization processing, used seed filling method to perform area filling, and used the image difference method to accurately extract the damaged area in inner tissue of mango fruit. Based on the deep transfer learning model, training and testing were carried out on unprocessed and processed mango image data. Transfer learning was carried out through the AlexNet and GoogLeNet deep learning networks, and hyperparameters were adjusted to complete the network fine-tuning of the training process. Under different models, the classification results of unprocessed and processed mango test sets on the model were compared.

Result  Based on the unprocessed data set, the GoogLeNet model was trained at a learning rate of 0.0002, and the Accuracy and Macro-average were 98.79% and 98.41% respectively. Based on the processed data set, the GoogLeNet model was trained at a learning rate of 0.0002, and the Accuracy and Macro-average were 100% and 100% respectively. The deep transfer learning model had a greater improvement in the model classification index of the processed data set than the unprocessed data set. Based on the same data set and consistent hyperparameters, the classification effect of the GoogLeNet network was significantly better than that of the AlexNet network.

Conclusion  While learning rate is set to 0.0002, the Epoch value is 3, and the Mini Batch value is 64, deep transfer learning training is carried out based on the GoogLeNet network, and the resulting model is used as the final classification model.