Future prospects

Deep learning and biosensors have exciting potential in the field of food quality inspection. (a).On the one hand, biosensors can quickly acquire internal features of food due to their unique sensing capabilities, and convolutional neural networks can quickly acquire external features of food, which can provide sufficient data for the training of predictive models. On the other hand, deep learning can automatically extract features of the target through its own network structure and transform low-level features into high-level features, avoiding the subjectivity of manual feature selection and saving a lot of time and workload. (b).In future research, it is expected to produce different food quality assessment systems based on this method. Food quality can be effectively and efficiently tested during the production, transportation, storage and sale of food. (c).Due to the embeddability of computer vision technology, by developing corresponding mobile applications and embedding convolutional neural networks into mobile applications, consumers can simply and accurately judge the quality of food to meet their own needs.