Information Package / Course Catalogue
| Course Code | : BSM317 |
| Course Type | : Area Elective |
| Couse Group | : First Cycle (Bachelor's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 2 |
| Prt. | : 2 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 5 |
With the developed algorithms and software in agricultural production; crop production planning, classification of plants, yield estimation, detection of plant diseases, pests and weeds, route determination in agricultural robots, determination of suitable environmental conditions in greenhouses, making business decisions, irrigation management, determination of crop rotation, selection of the most suitable fertilizer and tools-machinery, detection of animal diseases, preparation of appropriate feed rations, determination of animal behavior.
Applications of artificial intelligence and machine learning technologies in agriculture, determining the future of agriculture, adapting artificial intelligence to the processes of soil cultivation, planting, irrigation, crop care, soil / plant condition analysis and control. Artificial intelligence programs.
| Lec. Yüksel AYDOĞAN |
| 1. | How artificial intelligence is applied in agriculture, data analysis, machine learning and image processing |
| 2. | Analysis of agricultural data, prediction models and image processing techniques |
| 3. | It addresses the use of technologies such as sensors, drones and other smart farming equipment. |
| 4. | Explaining the use of machine learning techniques for sustainable agriculture, this book offers practical applications on topics such as productivity, soil management and pest control. |
| 5. | Recognize software used in artificial intelligence applications |
| 1. | Artificial Intelligence in Agriculture, Rajeev Sharma, Pradeep K. Shukla and Sanjeev Kumar |
| 2. | Data Science for Agriculture: Gloria Phillips-Wren, Anna Esposito, Lakhmi C. |
| 3. | Using Artificial Intelligence, Machine Learning and Image Processing Jain, and Roberto Revetria |
| 4. | Yapay Zekâ ve Akıllı Tarım Teknolojisi, Utku Köse |
| Type of Assessment | Count | Percent |
|---|---|---|
| Midterm Examination | 1 | %40 |
| Final Examination | 1 | %60 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 14 | 3 | 3 | 84 |
| Midterm Examination | 1 | 10 | 10 | 20 |
| Final Examination | 1 | 11 | 10 | 21 |
| TOTAL WORKLOAD (hours) | 125 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | PÇ-8 | PÇ-9 | PÇ-10 | PÇ-11 | |
OÇ-1 | 3 | 3 | 2 | 3 | 4 | ||||||
OÇ-2 | 3 | 3 | 2 | 3 | 4 | ||||||
OÇ-3 | 3 | 3 | 3 | 4 | |||||||
OÇ-4 | 3 | 2 | 3 | 4 | |||||||
OÇ-5 | 3 | 3 | 3 | 4 | |||||||