| Course Name |
(中) 農業資訊技術(3033) |
| (Eng.) Introduction to Agricultural Information Technology |
| Offering Dept |
International Bachelor Program in Agribusiness |
| Course Type |
Elective |
Credits |
3 |
Teacher |
Szu-Hung Chen |
| Department |
International Bachelor Program in Agribusiness/Undergraduate |
Language |
English |
Semester |
2025-SPRING |
| Course Description |
The course-Introduction to Agricultural Information Technology introduces students to the role of information technology in modern agriculture. It covers fundamental concepts of data management, precision agriculture, Geospatial Information Science and Technology, decision support systems, digital extension services, and more. Through case studies and hands-on activities, students will learn how information technology enhances productivity, sustainability, and decision-making in agricultural systems. |
| Prerequisites |
|
self-directed learning in the course |
Y |
| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
Through the course, students will be able to:
1. Understand the fundamental concepts of agricultural information technology and its impact on modern farming.
2. Learn various types of agricultural data and their applications in decision-making.
3. Accumulate the knowledge of data-driven decision-making in agricultural planning and production.
4. Develop a basic project that applies information and data-driven solutions to agricultural challenges. |
|
|
| topic Discussion/Production |
| Exercises |
| Discussion |
| Lecturing |
|
| Written Presentation |
| Oral Presentation |
| Assignment |
| Quiz |
| Other |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Introduction and Fundamental Concepts (I)
1. Course introduction
2. Definition and fundamental concepts of agricultural informatics. |
| Week 2 |
Introduction and Fundamental Concepts (II)
1. Information Technology vs. Agricultural Information Technology
2. Overview and historical development of IT applications in agriculture. |
| Week 3 |
Agricultural Data Collection and Processing
1.Role of data in modern farming
2.Types of agricultural data (e.g, weather, soil, crop or livestock productivity)
*In-class exercise: Agricultural data collection, editing and synthesis |
| Week 4 |
Agricultural Data Statistics and Analysis
1. Charts/diagrams creation of agricultural statistics
2. Interpretation and application of agricultural statistical information |
| Week 5 |
Agricultural Data Management and Databases (I)
1. Basic concepts and operations of database
2. Agricultural database development and management |
| Week 6 |
Guest Speaker: AI and database applications in modern agriculture
(Topic/Schedule: tentative; Further detail will be provided in class)
In-class exercise (tentative): How to use Google NotebookLM |
| Week 7 |
Agricultural Data Management and Databases (II)
1. Application of Agricultural Database and Information System
2. Other related data informatics (e.g., cloud-based platform) |
| Week 8 |
Geographic Information Systems (GIS) and Remote Sensing in Agriculture
1.Basics of GIS and mapping in agriculture
2. Role of remote sensing in agriculture
*In-class case study discussion: GIS or/and remote sensing in agriculture production (e.g., crop monitoring, site selection or farmland management) |
| Week 9 |
Digital Extension and Mobile Applications in Agriculture
1.Role of ICT in agricultural extension services
2.Mobile apps for farmers (e.g., weather forecasting, pest management)
3.E-agriculture and knowledge-sharing platforms |
| Week 10 |
Mid-term Exam |
| Week 11 |
Smart Farming and Internet of Things (IoT) in Agriculture
1.Concept of smart farming
2.IoT-enabled farm management; for example:Use of drones, robotics and/or automated irrigation |
| Week 12 |
Precision Agriculture Technologies
1.Concepts of precision agriculture
2.Use of IoT devices, GPS, and sensors
3.Spatial data and GIS application in precision agriculture |
| Week 13 |
Challenges and Ethical Issues in Agricultural Information Technology
For example : Data privacy and cybersecurity in farm management,environmental and ethical concerns
*In-class discussion (Tentative) |
| Week 14 |
Future Trends in Agricultural Information Technology
1.Emerging trends: AI, blockchain, cloud computing
2.Future challenges and career opportunities in Agri-IT |
| Week 15 |
Final Project Development
*Toolkit: Google NotebookLM |
| Week 16 |
Final Project Presentation (Group/Individual) |
| Week 17 |
Self-learning Week: Completing the post-class self-reflection report (Individual) on key learnings
|
| Week 18 |
Self-learning Week: Video Watching |
|
| Evaluation |
| (1)Attendance: 10%; (2) In-class Activity (e.g., discussion, hand-on exercise):15%; (3) Assignments: 20%; (4)Midterm Exam: 25%; (5)Final Project and Presentation: 30% |
| Textbook & other References |
There is no mandatory textbook. Course handouts will be made available through NCHU iLearning if needed.
However, several selected references as below may be synthesized or cited in class materials. For examples:
[Example Reference #1] Snapp, S. S. and B. Pound.(eds.) 2017. Agricultural Systems: Agroecology and Rural Innovation for Development (2nd Edition). Academic Press, Burlington, MA
[Example Reference #2] Muller, T. and Sassenrath (eds.) 2015. GIS Applications in Agriculture, Volume Four: Conservation Planning. CRC Press, Boca Raton, UK. |
| Teaching Aids & Teacher's Website |
| NCHU iLearning may also be used to conduct quiz/exams, to submit course exercises, to broadcast announcements, and so on. It can be accessed via NCHU iLearning Portal (https://lms2020.nchu.edu.tw/ ) or NCHU Single Sign On System. |
| Office Hours |
| By Appointment |
| Sustainable Development Goals, SDGs(Link URL) |
| 02.Zero Hunger 04.Quality Education 09.Industry, Innovation and Infrastructure 10.Reduced Inequalities 11.Sustainable Cities and Communities | include experience courses：N |
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