Abstract
Food spoilage and human health are greatly affected by microorganisms, such as bacteria, algae, fungi, and protozoa. While traditional identification methods are reliable, they are often laborious and time-consuming. In recent years, artificial intelligence (AI) and image processing have made significant progress in identifying and classifying microorganisms quickly and accurately. In this review, we will examine image processing and artificial intelligencebased techniques for identifying and classifying microorganisms relevant to human health and food spoilage, comparing their effectiveness to traditional methods and assessing their impact on food safety. Bacteria, algae, fungi, and protozoa are the four major groups of microorganisms examined in this review. A review of applications in food safety, clinical microbiology, and environmental monitoring is presented in this paper. It examines how bacteria, yeast, and molds cause food spoilage and examines their mechanisms of action. Furthermore, the article highlights common foodborne illnesses and the health consequences of eating contaminated food. The paper also discusses advances in identifying spoilage-causing microorganisms, with a particular emphasis on artificial intelligence (AI) and image processing. With modern techniques, microbial contamination can be detected more accurately and efficiently, thus improving food safety. Finally, the review concludes by analyzing current challenges and future directions in the field, emphasizing the need for continued innovation in microbial detection methods. In the review, rapid detection of foodborne pathogens is highlighted, as well as automated spoilage monitoring. This technology has the potential to revolutionize food safety practices and clinical microbiology, so it must continue to be developed and validated.
Keywords: Algae, Bacteria, Food Spoilage, Fungi, Human Health, Impact of Microorganisms, Microorganisms, Protozoa