Detection of Bacteria on Food using Deep Learning

Due to their large global prevalence, foodborne illnesses have a significant negative impact on both the social economy and public health. Foodborne pathogens, the primary cause of foodborne illnesses, are crucial to the management and avoidance of foodborne illnesses. However, there is a limited percentage of true clinical pathogen detection in real life, and foodborne illnesses caused by many bacteria lack specificity in their clinical symptoms. Our objectives were to categorise foodborne illness pathogens using gas sensors and machine learning techniques, analyse foodborne disease case data, and choose relevant characteristics based on analysis findings in order to predict foodborne diseases.