Information Package / Course Catalogue
| Course Code | : KOC136 |
| Course Type | : Non Departmental Elective |
| Couse Group | : Short Cycle (Associate's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 2 |
| Prt. | : 0 |
| Credit | : 2 |
| Lab | : 0 |
| ECTS | : 2 |
The objective of this course is to introduce students to the concept of the digital twin in modern agriculture, to teach the methods of creating digital replicas of physical agricultural assets (such as greenhouses, tractors, crops, etc.), and to enable them to comprehend the pivotal role of this technology in productivity, sustainability, and decision-support processes.
Students who successfully complete this course will be able to: Define the concept of the digital twin and its position within Agriculture 4.0. Explain agricultural sensors and data acquisition (IoT) systems within the context of digital twins. Comprehend the digital modeling stages of a physical agricultural system. Perform baseline simulation and analysis using digital twin data.
| 1. | Defines the concept of the digital twin; explains its relationship with Agriculture 4.0, the Internet of Things (IoT), and Big Data. |
| 2. | Comprehends sensor technologies used in agricultural ecosystems (soil moisture, temperature, NDVI, etc.) and how these data are transferred into a digital model in real time. |
| 3. | Analyzes the processes of creating geometric and functional representations of a physical agricultural asset (e.g., a greenhouse unit or agricultural machinery) in a digital environment. |
| 4. | Simulates data derived from a digital twin. |
| 5. | Formulates solution proposals for efficiency improvement, resource conservation (water, fertilizer, energy), and early warning systems by leveraging the gathered digital data. |
| 1. | Instructor’s Lecture Notes |
| Type of Assessment | Count | Percent |
|---|---|---|
| Attending Lectures | 1 | %5 |
| Assignment | 1 | %5 |
| Midterm Examination | 1 | %30 |
| Final Examination | 1 | %60 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 0 | 2 | 28 |
| Individual Work | 5 | 2 | 1 | 15 |
| Midterm Examination | 1 | 1 | 1 | 2 |
| Final Examination | 1 | 1 | 1 | 2 |
| TOTAL WORKLOAD (hours) | 47 | |||
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 | |
OÇ-1 | 4 | |||||||||||||
OÇ-2 | 4 | |||||||||||||
OÇ-3 | 4 | |||||||||||||
OÇ-4 | 4 | |||||||||||||
OÇ-5 | 4 | |||||||||||||