國立中興大學教學大綱
課程名稱 (中) 農業資訊技術(3033)
(Eng.) Introduction to Agricultural Information Technology
開課單位 國農企學程
課程類別 選修 學分 3 授課教師 陳思宏
選課單位 國農企學程 / 學士班 授課使用語言 英文 開課學期 1142
課程簡述 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.
先修課程名稱
課程與核心能力關聯配比(%) 課程目標之教學方法與評量方法
課程目標 核心能力 配比(%) 教學方法 評量方法
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.
1.永續農業的基本知識與技能 Basic knowledge and skills in sustainable agriculture
2.農企業經營基本知識與技能 Basic knowledge and skills in agribusiness
50
50
專題探討/製作
習作
討論
講授
書面報告
口頭報告
作業
測驗
其他
授課內容(單元名稱與內容、習作/每週授課、考試進度-共16週加自主學習)
週次 授課內容
第1週 Introduction and Fundamental Concepts (I)
1. Course introduction
2. Definition and fundamental concepts
第2週 Introduction and Fundamental Concepts (II)
1. Information Technology vs. Agricultural Information Technology
2. Overview and historical development of IT applications in agriculture.
第3週 Agricultural Data Collecting, Processing and Management (I)
1.Role of data in modern farming
2.Types of agricultural data (e.g, weather, soil, crop or livestock productivity

*Hand-on exercise: Agricultural data searching and collecting
第4週 Agricultural Data Collecting, Processing and Management (II)

*Hand-on exercise : Agricultural data processing and synthesis
第5週 Guest Speaker: AI and database applications in modern agriculture
(Topic/Schedule: tentative; Further detail will be provided in class)

*AI Toolkit exercise (tentative): Google NotebookLM、 Google Antigravity
第6週 Agricultural Data Statistics and Analysis
1. Charts/diagrams creation of agricultural statistics
2. Interpretation and application of agricultural statistical information
第7週 Make-up holiday for University Anniversary and Annual Sports Meet
*No on-site class; take-home exercise may be assigned
第8週 Agricultural Data Management and Databases
1. Basic concepts and operations of database
2. Agricultural database development and management
3. Application of Agricultural Database and Information System
第9週 Mid-term Exam
第10週 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-clas discussion: GIS or/and remote sensing in agriculture production (e.g., crop monitoring, site selection or farmland management)
第11週 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
3.Role of ICT in agricultural extension services
第12週 Final Project Instruction；
Challenges and Ethical Issues in Agricultural Information Technology (e.g., Data privacy and cybersecurity in farm management,environmental and ethical concerns)

*In-class discussion (Tentative): Final Project Group Arrangement
第13週 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
第14週 Future Trends in Agricultural Information Technology
1.Emerging trends: AI, blockchain, cloud computing
2.Future challenges and career opportunities in Agri-IT

*Final Project Development
第15週 Final Project Development
第16週 Final Project Presentationn (Group/Individual) and Post-class self-reflection report (Individual)
自主學習
內容		    02.閱覽產業及學術相關多媒體資料
   03.製作專題報告
學習評量方式
(1)Attendance: 10%; (2) Exercise and Assignment (e.g., discussion, hand-on exercise):30 %; (3)Midterm Exam: 30%; (5)Final Project Development : 30% (20% Group Presentation+ 10% self-reflection report)
教科書＆參考書目(書名、作者、書局、代理商、說明)
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.
課程教材（教師個人網址請列在本校內之網址）
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.
課程輔導時間
By Appointment
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更新日期 西元年/月/日：2026/02/09 20:14:38 列印日期 西元年/月/日：2026 / 3 / 15
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