Akademik Veri Yönetim Sistemi
42. Yöneylem Araştırması ve Endüstri Mühendisliği Kongresi, Gaziantep, Türkiye, 1 - 03 Kasım 2023, ss.67
In contemporary food industries, the concept of quality control is paramount for businesses, considering consumer safety, product consistency, taste, and adherence to industry standards. In these sectors, executing quality control via traditional and manual methods tends to be time-consuming and costly. Hence, transitioning from manual techniques to artificial intelligence-based quality control methods is becoming imperative for modern industries. To demonstrate the potential of transitioning to AI-based quality control systems in the food sector, we conducted a study. In this study, we automated the quality control of potato chips using a deep learning technique known as Convolutional Neural Networks (CNN). We developed a predictive model using 961 potato chip images provided by “PepsiCo Company”. This model rigorously analyzes the physical characteristics of potato chips to swiftly and objectively ascertain their compliance with predetermined quality standards. Our findings underscore the feasibility and effectiveness of utilizing artificial intelligence and deep learning as reliable instruments in food quality control.