Information Package / Course Catalogue
| Course Code | : TMT156 |
| Course Type | : Area Elective |
| 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 enable students to comprehend the concepts of smart agriculture, precision agriculture, and digital agriculture from an engineering perspective, to understand the data-driven decision-making approach in agricultural production, and to evaluate smart agriculture technologies within an integrated framework of data collection, data transmission, data processing, decision-making, and application systems.
Within the scope of the course, the concepts of smart agriculture, precision agriculture, and digital agriculture will be covered based on field variability, spatial data, sensor data, and image data. The utilization areas of GNSS, GIS, yield mapping, variable rate application, remote sensing, proximal sensing, agricultural drones, LiDAR, cameras, and sensor systems in agricultural production will be examined. Furthermore, the role of information and communication technologies such as IoT, LoRaWAN, wireless sensor networks, cloud computing, and decision support systems in smart agriculture practices will be evaluated.
| 1. | Define the concepts of smart agriculture, precision agriculture, and digital agriculture; explain their role and significance in agricultural production. |
| 2. | Explain the concepts of spatial data, sensor data, image data, and field variability in agricultural production. |
| 3. | Interpret the principles of electromagnetic spectrum, plant spectral behavior, remote sensing, and proximal sensing in terms of agricultural applications. |
| 4. | Evaluate the implementation and usage areas of GNSS, GIS, yield mapping, and variable rate application technologies in smart farming. |
| 5. | Analyze the use of sensors, agricultural drones, LiDAR, cameras, IoT, LoRaWAN, cloud computing, and decision support systems in smart agriculture infrastructures from an engineering perspective. |
| 1. | Sustainable Smart Agricultural Technologies, Ekin Publishing |
| 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 | 3 | 4 | 4 | 3 | 4 | 5 | 3 | 3 | 3 | 3 | 3 | 2 |
OÇ-2 | 3 | 4 | 4 | 4 | 5 | 4 | 4 | 4 | 3 | 3 | 2 | 2 |
OÇ-3 | 3 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 2 | 2 |
OÇ-4 | 3 | 4 | 4 | 5 | 5 | 3 | 4 | 3 | 4 | 3 | 3 | 2 |
OÇ-5 | 3 | 3 | 4 | 5 | 5 | 4 | 3 | 3 | 3 | 3 | 2 | 2 |