Information Package / Course Catalogue
Smart Farm
Course Code: BSM428
Course Type: Required
Couse Group: First Cycle (Bachelor's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 0
Credit: 2
Lab: 0
ECTS: 3
Objectives of the Course

Data collection, enter data on database according to the rules of Geographic Information System (GIS), producing a new data and information, gain skills for analyzing and interpreting this information

Course Content

Use of geographic information systems for identification of objects spreaded over large areas, the identification and mapping of their places teach basic information, methods, software and hardware used for this purpose.

Name of Lecturer(s)
Prof. Saadettin YILDIRIM
Learning Outcomes
1.Defines the concepts of smart agriculture, precision agriculture and digital agriculture, and explains their role in agricultural production.
2.Explains the concepts of spatial data, sensor data, image data and field variability in agricultural production.
3.Interprets the electromagnetic spectrum, plant spectral behavior, remote sensing and proximal sensing principles in terms of agricultural applications.
4.Evaluates the use of GNSS, GIS, yield mapping and variable-rate application technologies in smart agriculture.
5.Analyzes the use of sensors, agricultural drones, LiDAR, cameras, IoT, LoRaWAN, cloud computing and decision support systems in smart agriculture from an engineering perspective.
Recommended or Required Reading
1.Morgan, M., & Ess, D. (2003). The Precision-Farming Guide for Agriculturists (2nd ed.). John Deere Publishing.
2.Zhang, Q. (Ed.). (2015). Precision Agriculture Technology for Crop Farming. CRC Press.
3.Campbell, J. B., Wynne, R. H., & Thomas, V. A. (2022). Introduction to Remote Sensing (6th ed.). Guilford Press.
4.GPS.gov. The Global Positioning System, GPS accuracy and augmentation systems. U.S. government official GPS website.
5.NASA Earthdata / NASA Earth Science. Remote sensing and agricultural applications. National Aeronautics and Space Administration.
6.Lecture notes and current scientific articles.
Weekly Detailed Course Contents
Week 1 - Theoretical
Concepts of smart agriculture, precision agriculture and digital agriculture
Week 2 - Theoretical
Digital transformation in agriculture, Agriculture 4.0/5.0 and information and communication technologies
Week 3 - Theoretical
Spatial and temporal variability, data collection, analysis and management of variability
Week 4 - Theoretical
Global navigation satellite systems, error sources in positioning and error correction methods (DGPS, RTK, CORS, SBAS, etc.)
Week 5 - Theoretical
Geographic Information Systems, spatial data management and agricultural applications
Week 6 - Theoretical
Yield mapping and variable-rate application technologies
Week 7 - Theoretical
Electromagnetic spectrum and spectral behavior of plants
Week 8 - Theoretical
Principles of remote sensing and agricultural applications
Week 9 - Theoretical
Satellite- and agricultural drone-based imaging systems
Week 10 - Theoretical
Proximal sensing, portable measurement devices and in-field measurement applications
Week 11 - Theoretical
Spectral cameras, thermal imaging and LiDAR technologies in agricultural applications
Week 12 - Theoretical
IoT, LoRaWAN, wireless sensor networks and cloud computing
Week 13 - Theoretical
Decision support systems and examples of smart agriculture applications
Week 14 - Theoretical
General evaluation of smart agriculture technologies
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142256
Midterm Examination1718
Final Examination110111
TOTAL WORKLOAD (hours)75
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
OÇ-1
2
2
2
2
1
1
1
OÇ-2
3
3
1
2
1
2
OÇ-3
2
2
2
2
2
1
1
1
OÇ-4
2
2
1
1
1
OÇ-5
2
3
2
2
2
Adnan Menderes University - Information Package / Course Catalogue
2026