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Information
| Unit | FACULTY OF AGRICULTURE |
| PEDOLOGY AND PLANT FEEDING PR. | |
| Code | BBT415 |
| Name | Artificial Intelligence in Agriculture |
| Term | 2026-2027 Academic Year |
| Semester | 7. Semester |
| Duration (T+A) | 1-1 (T-A) (17 Week) |
| ECTS | 3 ECTS |
| National Credit | 1.5 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. Yakup Kenan KOCA |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The goal is to learn and gain knowledge about artificial intelligence (AI) techniques. This includes learning about AI applications in agriculture and developing the skills to utilize them in new agricultural fields.
Course Content
Teaching artificial intelligence techniques and methods. Teaching artificial intelligence applications used in agriculture.
Course Precondition
There are no prerequisites.
Resources
Ozguven, M.M., 2023. The Digital Age in Agriculture. CRC Press Taylor & Francis Group LLC. ISBN 978-103-23-8577-8. Yılmaz, A. 2023. Yapay Zeka. KODLAB yayınları. 9786059118804.
Notes
Agriculture 5.0 and Strategic Transformation in Türkiye: An AI-Powered Multidimensional Decision and Recommendation Model. Mehmet Yavuzer, Cinius Publications.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | He/She defines artificial intelligence. |
| LO02 | He/She describes the application areas of artificial intelligence. |
| LO03 | He/She describes the agricultural use of artificial intelligence. |
| LO04 | It supports the use of artificial intelligence in agricultural practices. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Has discipline-specific subject’s adequate knowledge of mathematics, science, and Agricultural Engineering (Soil Science and Plant Nutrition); use theoretical and applied knowledge in these fields of the complex engineering problems. | 3 |
| PLO02 | Beceriler - Bilişsel, Uygulamalı | Define, formulate and solve complex problems in the field of Soil Science and Plant Nutrition, select and apply appropriate analysis and modeling methods for this purpose. | |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions, and apply modern design methods for this purpose in Soil Science and Plant Nutrition discipline. | 2 |
| PLO04 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Select and use modern tools necessary for the analysis and solution of complex problems and use information technologies effectively in the field of Soil Science and Plant Nutrition application. | 3 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Design, conduct experiments, collect data, analyze and interpret results for the study of complex problems or discipline-specific research issues encountered in the field of Soil Science and Plant Nutrition. | |
| PLO06 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Work effectively in interdisciplinary (Soil Science and Plant Nutrition) and multidisciplinary teams; develope individual study skills | |
| PLO07 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Communicate effectively orally and in writing; has a foreign language knowledge at the “beginner” level; write reports effectively in the field of Soil Science and Plant Nutrition, understand written reports, prepare, design and production reports, make effective presentations, take and give clear and understandable instructions. | |
| PLO08 | Yetkinlikler - Öğrenme Yetkinliği | Gain awareness of the necessity of lifelong learning; access information in the field of Soil Science and Plant Nutrition, follow the developments in science and technology, and constantly renew oneself. | |
| PLO09 | Yetkinlikler - Alana Özgü Yetkinlik | Compliance with ethical principles, professional and ethical responsibility in the field of Soil Science and Plant Nutrition, and has knowledge of standards used in engineering practices. | |
| PLO10 | Yetkinlikler - Alana Özgü Yetkinlik | Gain knowledge of business practices as project and risk management and change management; gain awareness of entrepreneurship and innovation; information about sustainable development in the field of Soil Science and Plant Nutrition. | |
| PLO11 | Yetkinlikler - Alana Özgü Yetkinlik | Has information about the effects of Soil Science and Plant Nutrition practice’s on health, environmental, and security in universal scale and social dimensions: The problems of the age reflection related with the field of Soil Science and Plant Nutrition; gain awareness of the legal implications of Soil Science and Plant Nutrition solutions. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | What is artificial intelligence? | No preparation is required | Öğretim Yöntemleri: Anlatım |
| 2 | The level of artificial intelligence in technological advancements. | Current source research | Öğretim Yöntemleri: Anlatım |
| 3 | What are the methods of artificial intelligence? | No preparation is required | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 4 | What is machine learning? | Current source research | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 5 | What are the different types of machine learning? | No preparation is required | Öğretim Yöntemleri: Anlatım, Tartışma, Soru-Cevap |
| 6 | What are the stages of machine learning? | Researching current information. | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Tartışma |
| 7 | What is deep learning? | Researching current information. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 8 | Mid-Term Exam | Studying the topics covered in the first 7 weeks. | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Convolutional Neural Network | No preparation is required | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Types of Deep Learning Algorithms | Current source research | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Deep Learning in Agricultural Applications | Researching current information. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 12 | Model Performance Metrics | No preparation is required | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 13 | Examples of the Use of Artificial Intelligence in Agriculture - Part 1 | Current source research | Öğretim Yöntemleri: Anlatım |
| 14 | Examples of the Use of Artificial Intelligence in Agriculture - Part 2 | No preparation is required | Öğretim Yöntemleri: Anlatım |
| 15 | Examples of the Use of Artificial Intelligence in Agriculture - Part 3 | No preparation is required | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 2 | 28 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 1 | 14 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 1 | 1 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 10 | 10 |
| Final Exam | 1 | 10 | 10 |
| Total Workload (Hour) | 63 | ||
| Total Workload / 25 (h) | 2,52 | ||
| ECTS | 3 ECTS | ||