Deep learning and multiwavelength fluorescence imaging for cleanliness assessment and disinfection in Food Services

Precise, reliable, and speedy contamination detection disinfection is an ongoing challenge for the food-service industry. Contamination in food-related services can cause foodborne illness, endangering customers jeopardizing provider reputations. Fluorescence imaging has been shown to be capable of identifying organic residues biofilms that host pathogens. We use new fluorescence technology, applying Xception DeepLabv3+ deep learning algorithms identify segment contaminated areas images equipment surfaces. Deep models demonstrated a 98.78% accuracy differentiation between clean frames on various surfaces resulted intersection over union (IoU) score 95.13% segmentation contamination. The portable system’s intrinsic capability was evaluated S. enterica, E. coli, L. monocytogenes , resulting up 8-log reductions under 5 s. Results showed with could help assure safety cleanliness