Information Package / Course Catalogue
| Course Code | : TMT255 |
| 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 | : 2 |
The objective of this course is to teach the fundamental principles of autonomous agricultural vehicles and agricultural robotic systems, to introduce sensor technologies, artificial intelligence, computer vision, navigation, and control systems, and to enable students to gain an engineering perspective for analyzing, evaluating, and developing autonomous machinery used in agricultural production.
Introduction to agricultural robotics and autonomous systems, basic components of robotic systems, sensors and actuators, GPS/GNSS-based navigation systems, LiDAR, camera and image processing systems, computer vision, artificial intelligence and machine learning applications, autonomous tractors, agricultural mobile robots, robotic spraying and harvesting systems, field robots, greenhouse robots, decision support systems, IoT-based robotic applications, safety systems, ethical and legal regulations, and current agricultural robotics applications.
| 1. | Explains the basic operating principles, components, and application areas of autonomous agricultural vehicles and agricultural robots. |
| 2. | Analyzes sensors, actuators, GNSS, LiDAR, cameras, and control systems used in agricultural robots. |
| 3. | Evaluates autonomous agricultural applications based on artificial intelligence, computer vision, and machine learning. |
| 4. | Analyzes autonomous tractors, mobile agricultural robots, and robotic harvesting, spraying, and maintenance systems from an engineering perspective. |
| 5. | Develops innovative solution proposals for autonomous agricultural systems by considering safety, ethics, sustainability, energy efficiency, and legal regulations. |
| 1. | Autonomous Vehicles in the Age of Artificial Intelligence and the Product Liability of the Autonomous Vehicle Manufacturer |
| 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 |
| Assignment | 1 | 5 | 0 | 5 |
| Individual Work | 5 | 0 | 1 | 5 |
| Midterm Examination | 1 | 4 | 1 | 5 |
| Final Examination | 1 | 6 | 1 | 7 |
| TOTAL WORKLOAD (hours) | 50 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | PÇ-12 | |
OÇ-1 | 4 | 4 | 4 | 5 | 5 | 4 | 5 | 5 | 5 | 4 | 2 | 2 |
OÇ-2 | 3 | 4 | 4 | 4 | 5 | 3 | 4 | 4 | 4 | 4 | 2 | 2 |
OÇ-3 | 4 | 4 | 4 | 5 | 5 | 4 | 5 | 5 | 4 | 5 | 2 | 2 |
OÇ-4 | 4 | 4 | 4 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 2 | 2 |
OÇ-5 | 4 | 4 | 4 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 2 | 2 |