ไทยTH
UAT 489 · 3(1-4-4) · Year/term 4/1

UAS Technology for Smart Farming and Precision Agriculture

เทคโนโลยีระบบอากาศยานไร้คนขับเพื่อเกษตรอัจฉริยะและเกษตรแม่นยำ

Progress
Notional hours: 135 h (online/self-study 60 · in class/lab 75)

Course description

Thai description

Description source: Awaiting description in the curriculum document
Notes for the curriculum committee
  • เอกสารหลักสูตรฉบับ 28 ก.ย. 2569 ยังไม่มีคำอธิบายรายวิชา

Course learning outcomes (CLO)

CLOOutcomePLO
CLO1Apply UAS to smart farming and precision agriculturePLO5
CLO2Analyse multispectral data and vegetation indices for variable-rate field managementPLO4
CLO3Plan spraying or seeding safely and lawfullyPLO3PLO6

Learning modules

1Precision agriculture and agricultural drones
Weeks 1–3 · 27 h1 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 15 h

Intensive lab and field practice recorded in a lab notebook

Learning evidence: Lab notebook signed by the instructor

Open module
2Multispectral data
Weeks 4–6 · 27 h2 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 15 h

Intensive lab and field practice recorded in a lab notebook

Learning evidence: Lab notebook signed by the instructor

Open module
3Vegetation indices and prescription maps
Weeks 7–9 · 27 h2 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 15 h

Intensive lab and field practice recorded in a lab notebook

Learning evidence: Lab notebook signed by the instructor

Open module
4Variable-rate spraying and seeding
Weeks 10–12 · 27 h2 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 15 h

Intensive lab and field practice recorded in a lab notebook

Learning evidence: Lab notebook signed by the instructor

Open module
5Spatial decision-making for farms
Weeks 13–15 · 27 h2 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 15 h

Intensive lab and field practice recorded in a lab notebook

Learning evidence: Lab notebook signed by the instructor

Open module

Assessment (draft)

Laboratory performance50%
Reports and lab notebook20%
Practical examination30%

Knowledge domain

D04 Sensors and embedded systemsD05 Aircraft, structures and designD12 Automation, robotics and swarmsD13 Surveying, mapping and geoinformatics

Key references

  1. Food and Agriculture Organization & International Telecommunication Union. (2018). E-agriculture in action: Drones for agriculture. FAO. link
  2. Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation (7th ed.). Wiley. link
  3. NASA Earth Observatory. (2000, August 30). Measuring vegetation (NDVI & EVI). link