Information Package / Course Catalogue
Iot and Smart Systems in Animal Production
Course Code: DJTA102
Course Type: Required
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 1
Credit: 3
Lab: 0
ECTS: 4
Objectives of the Course

To develop competencies in using Internet of Things (IoT) technologies for monitoring and optimizing livestock operations.

Course Content

Topics include animal monitoring systems, sensor applications, automated feeding/milking, and smart barn technologies

Name of Lecturer(s)
Learning Outcomes
1.Describes IoT-based livestock systems.
2.Explains sensors for animal behavior and health monitoring.
3.Analyzes feeding, milking, and environmental control using digital systems.
4.Evaluates the effects of smart livestock applications on productivity.
5.Plans sustainable animal husbandry with IoT-supported systems.
Recommended or Required Reading
1.Atılgan, A. (2021). Smart Agricultural Technologies.
2.Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.J. (2017). Big Data in Smart Farming – A review. Agricultural Systems.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction to digitalization in animal production and historical background
Week 1 - Practice
Introduction to IoT components and demonstration of example systems
Week 2 - Theoretical
Concept of IoT and its role in livestock management
Week 2 - Practice
Analysis of sensor data in milk production
Week 3 - Theoretical
Classification of smart systems: monitoring, control, automation
Week 3 - Practice
Development of an automated feeding system scenario
Week 4 - Theoretical
Sensors and software in milk production
Week 4 - Practice
Animal identification using RFID applications
Week 5 - Theoretical
Automation and data management in feeding systems
Week 5 - Practice
GPS-based location tracking example
Week 6 - Theoretical
Animal health monitoring using RFID, GPS, and biosensors
Week 6 - Practice
Interpretation of biosensor data
Week 7 - Theoretical
Monitoring barn environmental parameters
Week 7 - Practice
Sampling of barn temperature, humidity, and gas levels
Week 8 - Theoretical
Digital solutions for heat stress detection and control
Week 8 - Practice
Creation of a heat stress map
Week 9 - Theoretical
Reproductive tracking: estrus detection and calving monitoring
Week 9 - Practice
Digital processing of reproductive data
Week 10 - Theoretical
Data analytics and decision support systems
Week 10 - Practice
Uploading and analyzing data via decision support software
Week 11 - Theoretical
IoT-based early warning systems
Week 11 - Practice
Scenario design for early warning systems
Week 12 - Theoretical
Cybersecurity and data protection in livestock systems
Week 12 - Practice
Data loss scenarios and cybersecurity solutions
Week 13 - Theoretical
Challenges in the application of smart technologies
Week 13 - Practice
Review of a sample smart farm case
Week 14 - Theoretical
General review and evaluation
Week 14 - Practice
Assessment quiz and summary of applications
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment1%5
Quiz1%5
Midterm Examination1%30
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Lecture - Practice140114
Reading50630
Individual Work50525
Midterm Examination1022
Final Examination1022
TOTAL WORKLOAD (hours)101
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
PÇ-14
PÇ-15
PÇ-16
PÇ-17
PÇ-18
PÇ-19
PÇ-20
OÇ-1
2
3
OÇ-2
3
OÇ-3
OÇ-4
2
3
OÇ-5
Adnan Menderes University - Information Package / Course Catalogue
2026