Information Package / Course Catalogue
Smart Food Processing Systems
Course Code: ST320
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 0
Credit: 2
Lab: 0
ECTS: 2
Objectives of the Course

The aim of this course is to understand the integration of artificial intelligence, internet of things (IoT), machine learning and automation systems in food processing technologies and to learn how these technologies are applied in processes such as food safety, quality control, energy efficiency and traceability. It is aimed that students will be able to analyze innovative food production systems and design their own digital solutions.

Course Content

Basic concepts of smart systems Digital transformation in the food processing industry IoT-based production monitoring and quality control Artificial intelligence and machine learning applications Image processing, robotics and automation Real-time data collection and processing Smart sensor systems Industry 4.0 and Food 4.0 concepts Smart factories and cyber-physical systems Application examples and case studies

Name of Lecturer(s)
Assoc. Prof. Filiz YILDIZ AKGÜL
Learning Outcomes
1.Defines digital and smart technologies used in food processing systems.
2.Analyzes the impact of artificial intelligence and IoT-based systems on the food industry.
3.Classifies and evaluates smart sensors and data collection tools.
4.Applies image processing and automation systems in food quality control.
5.Interprets sustainable and innovative production systems within the framework of Industry 4.0.
Recommended or Required Reading
1.Rasooly, A., & Herold, K. E. (Eds.). Biosensors and Biodetection: Methods and Protocols – Springer, 2017.
2.Zhang, Y., & Kovacs, G. (Eds.). Smart Sensors for Real-Time Water Quality Monitoring – CRC Press, 2020.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction: Smart systems and Industry 4.0
Week 2 - Theoretical
IoT fundamentals and applications to food processes
Week 3 - Theoretical
Smart sensors: Types and data collection techniques
Week 4 - Theoretical
Image processing systems and quality assessment
Week 5 - Theoretical
Introduction to artificial intelligence algorithms
Week 6 - Theoretical
Prediction and classification with machine learning
Week 7 - Theoretical
Automation systems in food production
Week 8 - Theoretical
Digitalization in the food processing industry
Week 9 - Theoretical
Robotic applications: Selection, packaging, transportation
Week 10 - Theoretical
Real-time monitoring and data analytics
Week 11 - Theoretical
Smart packaging and traceability systems
Week 12 - Theoretical
Case studies: Dairy, juice, meat products
Week 13 - Theoretical
Food 4.0 concept and future applications
Week 14 - Theoretical
Student presentations / project discussions
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Project26216
Midterm Examination1415
Final Examination1415
TOTAL WORKLOAD (hours)54
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
OÇ-1
3
4
OÇ-2
3
3
4
OÇ-3
4
OÇ-4
3
4
OÇ-5
3
Adnan Menderes University - Information Package / Course Catalogue
2026