Information Package / Course Catalogue
| Course Code | : TMT205 |
| Course Type | : Required |
| Couse Group | : Short Cycle (Associate's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 1 |
| Prt. | : 1 |
| Credit | : 2 |
| Lab | : 0 |
| ECTS | : 2 |
The objective of this course is to teach the application areas of artificial intelligence technologies in agricultural production; to introduce machine learning, deep learning, computer vision, decision support systems, and smart agriculture applications; and to enable students to gain the skills to analyze and apply artificial intelligence-based agricultural technologies.
Basic concepts of artificial intelligence, introduction to machine learning and deep learning, data mining, agricultural big data, computer vision applications, identification of plant diseases through image processing, yield prediction models, AI-powered irrigation and fertilization systems, agricultural robots, autonomous tractors, unmanned aerial vehicles (UAVs), decision support systems, Internet of Things (IoT), digital agriculture platforms, artificial intelligence applications in sustainable agriculture, and current case studies.
| 1. | Evaluates the application areas of artificial intelligence, machine learning, and deep learning in agriculture by explaining their fundamental concepts. |
| 2. | Analyzes sensor, image, and environmental data obtained from agricultural production using artificial intelligence methods. |
| 3. | Utilizes computer vision and machine learning techniques in plant disease identification, yield prediction, and precision agriculture applications. |
| 4. | Evaluates AI-based decision support systems, agricultural robots, autonomous machinery, and smart farming technologies from an engineering perspective. |
| 5. | Develops innovative solution proposals by analyzing the technical, economic, ethical, and sustainability dimensions of artificial intelligence applications in agriculture. |
| 1. | Artificial Intelligence and Computer Vision through Projects |
| 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 | 1 | 14 |
| Lecture - Practice | 14 | 0 | 1 | 14 |
| Assignment | 1 | 5 | 0 | 5 |
| Individual Work | 5 | 0 | 1 | 5 |
| Midterm Examination | 1 | 5 | 0 | 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 | 3 | 4 | 4 | 4 | 4 | 4 | 4 | 3 | 2 |
OÇ-2 | 5 | 4 | 4 | 4 | 5 | 5 | 5 | 4 | 4 | 3 | 2 | 2 |
OÇ-3 | 4 | 4 | 5 | 5 | 4 | 4 | 5 | 3 | 5 | 3 | 3 | 2 |
OÇ-4 | 4 | 3 | 4 | 4 | 3 | 5 | 3 | 3 | 4 | 3 | 2 | 2 |
OÇ-5 | 4 | 3 | 3 | 3 | 3 | 3 | 3 | 4 | 3 | 4 | 2 | 2 |