Abstract
With the development of machine vision technology, deep learning and image recognition technology has become a research focus for agricultural product non-destructive inspection. During the ripening process, banana appearance and nutrients clearly change, causing damage and unjustified economic loss. A high-efficiency banana ripeness recognition model was proposed based on a convolutional neural network and transfer learning. Banana photos at different ripening stages were collected as a dataset, and data augmentation was applied. Then, weights and parameters of four models trained on the original ImageNet dataset were loaded and fine-tuned to fit our banana dataset. To investigate the learning rate’s effect on model performance, fixed and updating learning rate strategies are analyzed. In addition, four CNN models, ResNet 34, ResNet 101, VGG 16, and VGG 19, are trained based on transfer learning. Results show that a slower learning rate causes the model to converge slowly, and the training loss function oscillates drastically. With different learning rate updating strategies, MultiStepLR performs the best and achieves a better accuracy of 98.8%. Among the four models, ResNet 101 performs the best with the highest accuracy of 99.2%. This research provides a direct effective model and reference for intelligent fruit classification.