Food freshness recognition method based on improved ResNet model

Abstract

Objective: Solve the problems of low detection efficiency and poor accuracy in existing food freshness recognition methods. Methods: Based on the food production line image acquisition system, an improved residual neural network model was proposed for food freshness recognition on the production line. The improved LRELU activation function was introduced to improve the recognition performance of the model, the batch normalization layer was introduced to improve the training efficiency of the model, and the Dropout layer was introduced to discard a certain proportion of neurons to reduce the impact of over fitting. Results: Compared with conventional food freshness recognition methods, the experimental method could accurately and efficiently achieve food freshness recognition, with an overall freshness recognition accuracy of ＞97%, average recognition time of 9.8 ms, which meet the needs of food production lines for freshness recognition. Conclusion: The detection method based on deep learning is a non-destructive, efficient, and high-precision method for recognizing the freshness of food images.

Keywords

• food production line
• freshness identification
• resnet model
• lrelu activation function
• normalization layer