Information Package / Course Catalogue
Artificial Intelligence Applications in Food
Course Code: KGT274
Course Type: Area Elective
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 0
Credit: 2
Lab: 0
ECTS: 3
Objectives of the Course

The aim of this course is to enable students to learn the basic principles of artificial intelligence (AI) technologies in the food industry and to understand how these technologies are used in production, quality control, safety and consumer preference analysis. In addition, students will gain the ability to interpret simple data analysis and AI applications.

Course Content

Overview of AI Application areas of AI in the food industry Image processing and quality control Shelf life prediction with machine learning Prediction models with sensor data Consumer preference analysis Labeling, counterfeit detection Application examples and case studies

Name of Lecturer(s)
Learning Outcomes
1.Defines the basic concepts of artificial intelligence.
2.Explains AI applications in the food industry with examples.
3.Evaluates the use of AI in quality control, safety and forecasting processes.
4.Explains and applies basic data analysis processes.
5.Can present and discuss the application of artificial intelligence in a sample food product.
Recommended or Required Reading
1.Goodacre, R., & Kell, D. B. (2021). Artificial Intelligence in Food Quality Control. Elsevier.
2.Sun, D.-W. (Ed.). (2019). Computer Vision Technology for Food Quality Evaluation. Academic Press.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to artificial intelligence and basic concepts
Week 2 - Theoretical
Introduction to artificial intelligence and basic concepts
Week 3 - Theoretical
Introduction to artificial intelligence and basic concepts
Week 4 - Theoretical
Introduction to artificial intelligence and basic concepts
Week 5 - Theoretical
Types of artificial intelligence and learning approaches
Week 6 - Theoretical
AI use in the food industry: An overview
Week 7 - Theoretical
Data collection and preprocessing techniques
Week 8 - Theoretical
Artificial intelligence applications in the dairy sector
Week 9 - Theoretical
Artificial intelligence applications in the Red Meat Industry, Poultry Industry and Aquaculture Industry
Week 10 - Theoretical
Artificial intelligence applications in the Flour and Pasta Sectors
Week 11 - Theoretical
Artificial intelligence applications in the Fruit and Vegetable Processing Industry
Week 12 - Theoretical
Artificial intelligence applications in the Vegetable Oil and Margarine Sectors
Week 13 - Theoretical
Artificial intelligence applications in the Legume and Paddy Rice Sectors
Week 14 - Theoretical
rtificial intelligence applications in the Starch and Starch-Based Sugar Industry, Chocolate, Gum and Confectionery Industry and Honey Industry
Assessment Methods and Criteria
Type of AssessmentCountPercent
Presentation1%10
Assignment1%10
Midterm Examination1%20
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Assignment1718
Presentation 26216
Midterm Examination1819
Final Examination110111
TOTAL WORKLOAD (hours)72
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
PÇ-12
PÇ-13
OÇ-1
4
5
4
3
OÇ-2
5
5
5
3
OÇ-3
3
4
5
OÇ-4
5
4
5
4
OÇ-5
4
5
5
5
5
Adnan Menderes University - Information Package / Course Catalogue
2026