Computer Aided Image Recognition Technology for Food Microbial Detection

This article aims to explore the application potential of computer-aided image recognition technology in the field of food microbial detection, in order to overcome the limitations of traditional detection methods, such as long time-consuming and cumbersome operation. In order to achieve this goal, an image recognition system of food microorganisms based on advanced image processing algorithm and machine learning model is designed and constructed. In terms of methods, this article uses high-resolution microscope and digital camera to collect images, and uses image preprocessing, segmentation, feature extraction and other technologies to process images; Then, DL (Deep learning) model is applied to learn and classify the extracted microbial characteristics. The experimental results show that the recognition accuracy of the system for Escherichia coli, Salmonella and other food microorganisms is over 95%, and the detection time is significantly shorter than that of the decision tree method. This achievement proves the effectiveness and practicability of computer-aided image recognition technology in food microbial detection. It has a certain contribution to improving the detection ability of food microorganisms.