Abstract:
With the improvement of people's living standards, consumers' demand for food quality and safety is growing. Traditional methods for detecting food quality and safety can no longer meet the demand for efficient, accurate and reliable detection. Therefore, it becomes imperative to seek a more efficient and convenient detection method. On this basis, the rapid development of deep neural network-based machine learning technology, i.e., deep learning, has brought new opportunities for food quality and safety detection. This paper focuses on the progress of the application of deep learning in food quality and safety inspection, introduces the principles of traditional machine learning and deep learning, describes the application of deep learning in the traceability of food origin and the detection of food defects, freshness, adulteration and pathogens in food quality, and looks forward to the development trend of the development trend of deep learning in the field of food quality and safety inspection with a view to providing theoretical references for the food quality and safety inspection field by providing theoretical references and research ideas.