Information Package / Course Catalogue
Digitization in Agriculture
Course Code: TB231
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: 3
Objectives of the Course

With the developing technology, digital and smart agriculture systems called 4th Period agriculture have entered our lives. These technologies include smart irrigation systems, smart planting systems, unmanned aerial vehicles and pesticides and risk management determinations. Within the scope of this course, the achievements in the period called

Course Content

In the 2010s, with the revolution in industry with industry 4.0, information technologies began to be used intensively in agriculture. This process is a production where agronomists are empowered with a variety of tools and technologies that help them be more productive and efficient. The transition process from intensive use of labor to information power is in question in this period. Among the main objectives of Agriculture 4.0 is to make the most suitable production for low cost, fast, economic and expectation or market. For this purpose, economical technological planting vehicles, irrigation equipment and unmanned agricultural vehicles are used.

Name of Lecturer(s)
Learning Outcomes
1.1. Recognition of current technology in agriculture
2.2. Integration of smart systems in agricultural production
3.3. The ability to use unmanned vehicles in agricultural production
4.4. Facilitation of risk management and early diagnosis systems due to digitalization in agricultural production
5.5. Instant data flow and reporting methods with digital agricultural systems
Recommended or Required Reading
1.1. Zhang, Q. (2016). Precision agriculture technology for crop farming (p. 374). Taylor & Francis
2.1. Zhang, Q. (2016). Precision agriculture technology for crop farming (p. 374). Taylor & Francis. 2. Ozguven, M. M. (2023). The Digital Age in Agriculture. CRC Press. 3. Mouazen, A., Castrignano, A., Moshou, D., Buttafuoco, G., & andRaj Khosla, O. N. (2020). Agricultural internet of things and decision support for precision smart farming.
3.3. Mouazen, A., Castrignano, A., Moshou, D., Buttafuoco, G., & andRaj Khosla, O. N. (2020). Agricultural internet of things and decision support for precision smart farming.
Weekly Detailed Course Contents
Week 1 - Theoretical
ntroduction
Week 2 - Theoretical
introduction What is digitalization in agriculture?
Week 3 - Theoretical
Smart farming systems
Week 4 - Theoretical
Management of images and data obtained in field crop cultivation, machine learning techniques
Week 5 - Theoretical
Autonomous agricultural vehicles
Week 6 - Theoretical
Different unmanned aerial vehicles used in agricultural production, yield estimation methods with unmanned aerial vehicles in field crops cultivation
Week 7 - Theoretical
Principles of use of agricultural drones
Week 8 - Theoretical
Smart irrigation systems
Week 9 - Theoretical
Artificial intelligence and machine learning
Week 10 - Theoretical
Machine learning algorithms in agriculture
Week 11 - Theoretical
Smart data management in agriculture
Week 12 - Theoretical
Presentations
Week 13 - Theoretical
Seminars
Week 14 - Theoretical
The future of digitalization in agriculture
Assessment Methods and Criteria
Type of AssessmentCountPercent
Seminar1%10
Assignment1%10
Midterm Examination1%20
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory141242
Midterm Examination114115
Final Examination117118
TOTAL WORKLOAD (hours)75
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
5
4
OÇ-2
OÇ-3
OÇ-4
4
OÇ-5
Adnan Menderes University - Information Package / Course Catalogue
2026