YOLO-based Food Damage Detection: An Automated Approach for Quality Control in Food Industry

In modern food production and quality control, accuracy and efficiency are paramount. This study presents a pioneering application of artificial intelligence technology that integrates the advanced object recognition model YOLOv8 into food quality assessment. This approach uses high-resolution images for detection and aims to transform the food industry by providing real-time insights and actionable data to ensure quality. The main goal of this research is to create a comprehensive AI-based system that uses YOLOv8 and #039 capabilities to identify and locate spoiled food in complex and diverse production flows. Leveraging YOLOv8's state-of-the- art object detection capabilities, the approach enables rapid detection of damaged products and quality assessment, improving overall food quality control practices. In addition, YOLOv8 integration with cloud-based systems facilitates dynamic visualization and monitoring of key performance metrics. With these visualization tools, the system can provide real-time information about detection accuracy, model behavior, and potential biases. This continuous monitoring enables quick adjustments and refinements, resulting in an optimized and reliable food quality control solution. With the innovative fusion of YOLOv8 and cloud technology, the paper aims to promote accurate food quality control. By providing kitchen processors with timely and accurate information about product quality and potential damage, the system can transform traditional quality assurance practices and usher in a new era of information-driven food production.