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.