Application and optimization of deep learning algorithms in food microbial contamination detection

Food microbial contamination is one of the primary factors contributing to food safety incidents. Its high risk, concealment, and diversity pose significant challenges to traditional detection technologies. In recent years, with the development of artificial intelligence technology, deep learning has demonstrated exceptional capabilities in image recognition, data analysis, and pattern classification, providing a new approach for efficient and intelligent detection of food microbial contamination. This paper systematically reviews the main application scenarios of deep learning algorithms in food microbial detection, including image-based colony recognition, multimodal data fusion analysis, and reinforcement learning strategies in small-sample environments; and explores the optimization directions of current algorithms in actual deployment from aspects such as model lightweighting, data annotation optimization, and system generalization capabilities. Research indicates that deep learning technology not only enhances the accuracy and efficiency of microbial contamination detection but also possesses excellent scalability and engineering potential, providing strong support for the intelligent transformation of food safety supervision and early warning systems.