Information Package / Course Catalogue
Iot and Smart Systems in Plant Production
Course Code: DJTA105
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

It is aimed to provide knowledge and skills to optimize plant production with digital tools.

Course Content

IoT sensors, irrigation-fertilization automation, data collection, decision support systems and analysis topics are covered.

Name of Lecturer(s)
Learning Outcomes
1.Defines IoT technologies used in plant production.
2.Collects data such as soil moisture, temperature, and light using sensors.
3.Analyzes automatic irrigation and fertilization systems based on data.
4.Evaluates the effects of smart systems on productivity.
5.Implements digital decision support systems in field management.
Recommended or Required Reading
1.Keskin, T. (2021). Internet of Things and its Applications in Agriculture.
2.Zhang, Q. (Ed.). (2016). Precision Agriculture Technology for Crop Farming. CRC Press.
Weekly Detailed Course Contents
Week 1 - Theoretical
Digitalization in agriculture and the concept of IoT
Week 1 - Practice
Introduction to basic IoT tools used in digital agriculture
Week 2 - Theoretical
Overview of digital farming practices in crop production
Week 2 - Practice
Reading data from temperature and humidity sensors
Week 3 - Theoretical
IoT-based sensors: temperature, humidity, light, and soil sensors
Week 3 - Practice
Measuring light intensity and soil moisture
Week 4 - Theoretical
Definition and classification of automation systems
Week 4 - Practice
Examination of automatic ventilation systems in greenhouses
Week 5 - Theoretical
Smart control systems used in greenhouses
Week 5 - Practice
Scenario for setting up a sensor network in open fields
Week 6 - Theoretical
Data collection and analysis in open-field agriculture
Week 6 - Practice
Real-time data tracking via remote monitoring panel
Week 7 - Theoretical
Remote monitoring and real-time intervention possibilities
Week 7 - Practice
Visual presentation of plant development data
Week 8 - Theoretical
Role of IoT in monitoring plant growth processes
Week 8 - Practice
Irrigation decision scenario based on development data
Week 9 - Theoretical
Role of digital systems in decision support
Week 9 - Practice
Intervention via mobile application
Week 10 - Theoretical
Mobile applications and cloud-based platforms
Week 10 - Practice
Data transfer and access on cloud platform
Week 11 - Theoretical
Contribution of smart systems to economic efficiency
Week 11 - Practice
Preparation of economic analysis charts
Week 12 - Theoretical
Sustainability and energy management in digital farming
Week 12 - Practice
Evaluation of sustainability parameters
Week 13 - Theoretical
Data security and infrastructure requirements in IoT
Week 13 - Practice
Discussion of sample security vulnerabilities
Week 14 - Theoretical
General review and project presentations
Week 14 - Practice
Project presentation and practical evaluation
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
Reading60424
Individual Work60530
Midterm Examination1022
Final Examination1022
TOTAL WORKLOAD (hours)100
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
4
OÇ-2
3
2
OÇ-3
3
OÇ-4
3
OÇ-5
3
Adnan Menderes University - Information Package / Course Catalogue
2026