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Information
| Unit | |
| Code | SD0821 |
| Name | Artificial Intelligence and Smart Food Systems |
| Term | 2026-2027 Academic Year |
| Term | Fall and Spring |
| Duration (T+A) | 2-0 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Üniversite Dersi |
| Label | UCC University Common Course |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Öğr. Gör. BETÜL KILINÇLI |
| Course Instructor |
Öğr. Gör. BETÜL KILINÇLI
(Güz)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
The primary objective of this course is to introduce the fundamental concepts of smart food systems and artificial intelligence applications within the field of food engineering. It aims to develop competencies in the acquisition, organization, and management of data generated from food production processes using appropriate methodologies. The course further covers the principles and utilization of sensor technologies and monitoring systems, basic data analysis techniques, and the visualization of results through graphical and tabular representations. Additionally, students will be able to interpret analytical outputs within the context of food quality and safety.
Course Content
This course covers the definition and scope of smart food systems, digital transformation in the food industry, and the Food 4.0 paradigm. It includes fundamental concepts of artificial intelligence and their application areas in food engineering, types of food-related data and data acquisition methods, sensor technologies, and real-time monitoring systems. The course also addresses smart packaging applications, IoT-based food systems, and the use of image processing techniques in food quality assessment. Further topics include food quality control applications and defect detection, food safety practices and risk monitoring systems, case studies of artificial intelligence applications in the food industry, and the critical review and evaluation of recent scientific studies.
Course Precondition
Resources
Hassoun, A. (Ed.). (2024). Food Industry 4.0: Emerging trends and technologies in sustainable food production and consumption. Academic Press. Donis-Gonzalez, I. (2024). Sensors and real-time monitoring in food processing. Springer.
Notes
Kılınç, İ., & Durak, M. (Eds.). (2023). Digital transformation in the food industry and Food 4.0. Nobel Academic Publishing. Devadas, M. R., Hiremani, V., Jagannath, P. G., Ambreen, L., Patrick, H. A., Devadas, M. R. (2025). Sustainable Agriculture Applications Using Large Language Models. Bentham Science Publishers.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains the fundamental components and operating principles of smart food systems. |
| LO02 | It defines the application areas of artificial intelligence (machine learning, deep learning) in food engineering. |
| LO03 | Applies data collection, processing, and analysis methods in food production processes. |
| LO04 | It interprets sensor technologies and real-time monitoring systems. |
| LO05 | It analyzes AI-based solution approaches for food quality and safety problems. |
| LO06 | It performs quality classification and defect detection using image processing techniques. |
| LO07 | Develops data-driven models for the optimization of food processes. |
| LO08 | Explains the operational structure of IoT-based smart food systems. |
| LO09 | Analyzes current scientific literature and conducts a critical evaluation. |
| LO10 | It proposes innovative solutions to food engineering problems from an interdisciplinary perspective. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to the course and general concepts | Lecture notes and presentation | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 2 | Food 4.0 and digital transformation | Lecture notes and presentation | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 3 | Introduction to artificial intelligence | Lecture notes and presentation | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Tartışma |
| 4 | Artificial intelligence applications in food engineering | Lecture notes and presentation | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Tartışma |
| 5 | Food data and data types | Lecture notes and presentation | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Tartışma |
| 6 | Data collection methods | Lecture notes and presentation | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Tartışma |
| 7 | Data editing and basic analysis | Lecture notes and presentation | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Tartışma |
| 8 | Mid-Term Exam | Lecture notes and presentation | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Sensor technologies | Lecture notes and presentation | Öğretim Yöntemleri: Anlatım, Tartışma, Soru-Cevap |
| 10 | Smart packaging systems | Lecture notes and presentation | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 11 | IoT and smart food systems | Lecture notes and presentation | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 12 | Fundamentals of image processing | Lecture notes and presentation | Öğretim Yöntemleri: Soru-Cevap, Tartışma, Anlatım, Alıştırma ve Uygulama |
| 13 | Food safety practices | Lecture notes and presentation | Öğretim Yöntemleri: Alıştırma ve Uygulama, Gösteri, Soru-Cevap, Anlatım, Tartışma |
| 14 | Food quality control practices | Lecture notes and presentation | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Tartışma, Alıştırma ve Uygulama |
| 15 | Artificial intelligence applications in industry | Lecture notes and presentation | Öğretim Yöntemleri: Soru-Cevap, Tartışma, Alıştırma ve Uygulama, Anlatım |
| 16 | Term Exams | Lecture notes and presentation | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Lecture notes and presentation | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 2 | 28 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 2 | 28 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 6 | 6 |
| Final Exam | 1 | 10 | 10 |
| Total Workload (Hour) | 72 | ||
| Total Workload / 25 (h) | 2,88 | ||
| ECTS | 3 ECTS | ||