Information Package / Course Catalogue
Remote Sensing and Drone Applications
Course Code: DJTA104
Course Type: Required
Couse Group: Short Cycle (Associate's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 1
Credit: 3
Lab: 0
ECTS: 3
Objectives of the Course

To introduce students to remote sensing technologies and their application in precision agriculture.

Course Content

The course includes image types (RGB, multispectral), interpretation of data, drone operation, and crop condition monitoring.

Name of Lecturer(s)
Learning Outcomes
1.Defines the basic principles of remote sensing.
2.Identifies drone usage areas in agriculture.
3.Analyzes drone-captured images.
4.Maps plant stress conditions.
5.Supports agricultural decision-making with digital imaging.
Recommended or Required Reading
1.Gülci, S. (2020). Agriculture with Remote Sensing and Geographic Information Systems.
2.Jones, H.G. & Vaughan, R.A. (2010). Remote Sensing of Vegetation. Oxford University Press.
Weekly Detailed Course Contents
Week 1 - Theoretical
Definition, history, and basic concepts of remote sensing
Week 1 - Practice
Examination of remote sensing images with examples
Week 2 - Theoretical
Electromagnetic spectrum and its interaction with the Earth
Week 2 - Practice
Example interpretation of EM spectrum responses
Week 3 - Theoretical
Satellite systems, sensor types, and resolution
Week 3 - Practice
Comparative resolution analysis of satellite images
Week 4 - Theoretical
Introduction to image processing techniques
Week 4 - Practice
Basic settings in image processing software
Week 5 - Theoretical
Role of remote sensing in agricultural practices
Week 5 - Practice
Creating NDVI maps of agricultural land
Week 6 - Theoretical
NDVI and other vegetation indices
Week 6 - Practice
Introduction to drone types and technical differences
Week 7 - Theoretical
Introduction and classification of drone technologies
Week 7 - Practice
Demonstration of drone components
Week 8 - Theoretical
Drone components and working principles
Week 8 - Practice
Preparing a basic flight plan scenario
Week 9 - Theoretical
Flight planning and legal regulations
Week 9 - Practice
Application of legal rules in flight scenarios
Week 10 - Theoretical
Agricultural data collection and mapping
Week 10 - Practice
Data collection and pre-mapping from the field
Week 11 - Theoretical
Image analysis software
Week 11 - Practice
Transferring visual data to software and interpreting
Week 12 - Theoretical
Detection of plant growth and stress conditions
Week 12 - Practice
Differentiating between stressed and healthy zones
Week 13 - Theoretical
Pre-harvest decision support using drones
Week 13 - Practice
Preparing a harvest plan based on drone data
Week 14 - Theoretical
General review and project presentations
Week 14 - Practice
Project presentations and final practical evaluation
Assessment Methods and Criteria
Type of AssessmentCountPercent
Assignment1%5
Quiz1%5
Midterm Examination1%30
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Lecture - Practice140114
Reading20918
Midterm Examination1055
Final Examination1088
TOTAL WORKLOAD (hours)73
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
PÇ-12
PÇ-13
PÇ-14
PÇ-15
PÇ-16
PÇ-17
PÇ-18
PÇ-19
PÇ-20
OÇ-1
4
OÇ-2
2
OÇ-3
OÇ-4
OÇ-5
3
Adnan Menderes University - Information Package / Course Catalogue
2026