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Master’s in Drone Engineering for Precision Agriculture and Geomatics

Online master's programme of 1,900 hours in drone engineering for precision agriculture and geomatics, structured in 12 modules covering precision agriculture, agronomic remote sensing, platforms and sensors, flight planning, photogrammetry, crop analysis, AI, prescription maps, spraying drones, GIS and data management, and regulation and sustainability, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Understand the fundamentals of precision agriculture.

02

Apply agronomic remote sensing techniques.

03

Plan multispectral and photogrammetric flights.

04

Generate accurate geomatic products.

05

Analyse crop condition with AI.

06

Produce prescription maps for variable-rate application.

07

Integrate data into GIS and comply with regulations.

Profile

Who it's for

  • Precision agronomists.
  • Remote sensing technicians.
  • Geospatial analysts.
  • Agricultural drone pilots.
  • Agritech consultants.
  • Farm managers adopting data-driven practices.
  • Engineering or science graduates seeking to enter agriculture.
Standards & tools

What you'll work with

  • Implementing Regulation (EU) 2019/947 (rules and procedures for the operation of unmanned aircraft)
  • Directive 2009/128/EC (sustainable use of pesticides)
  • Royal Decree 1311/2012 (Spanish framework for the sustainable use of plant protection products)
  • Regulation (EU) 2021/2115 (Common Agricultural Policy strategic plans)
  • ISO 11783 ISOBUS (tractor and implement communication)
  • ISO 19115 (geographic information metadata)
  • Royal Decree 1054/2022 (digital farm record book)
  • Pix4Dfields
  • Agisoft Metashape
  • QGIS
  • DJI Terra
  • Python
Syllabus

12 modules · 1900 h

  1. 1.1Spatial and temporal variability
  2. 1.2The precision agriculture cycle
  3. 1.3Technologies: GNSS, sensors and VRT
  4. 1.4Economics of precision farming
  5. 1.5Agriculture 4.0 and digitalisation
  6. 1.6Agronomic data and its management
  7. 1.7The role of drones
  8. 1.8Success stories
  9. 1.9Current limitations to adoption
  10. 1.10Case study: variability diagnosis in a cereal plot
  1. 2.1The electromagnetic spectrum and vegetation
  2. 2.2Reflectance and spectral signatures
  3. 2.3Vegetation indices: NDVI, NDRE and SAVI
  4. 2.4Discrete-band multispectral sensors
  5. 2.5Hyperspectral sensors in agriculture
  6. 2.6Thermography and water stress
  7. 2.7Radiometric calibration with panels
  8. 2.8Atmospheric correction of images
  9. 2.9Comparison with Sentinel-2 satellites
  10. 2.10Lab: calculating indices from multispectral images
  1. 3.1Fixed-wing and multirotor drones
  2. 3.2MicaSense and Parrot Sequoia cameras
  3. 3.3Sunlight sensors
  4. 3.4LiDAR for woody crops
  5. 3.5Spraying drones and their characteristics
  6. 3.6Endurance and coverage
  7. 3.7Selection by crop
  8. 3.8Field maintenance
  9. 3.9Equipment costs
  10. 3.10Practical exercise: sensor comparison for vineyards
  1. 4.1Overlaps and resolution
  2. 4.2Flight altitude
  3. 4.3Lighting conditions
  4. 4.4Calibration panels
  5. 4.5Ground control points
  6. 4.6Planning with software
  7. 4.7Data capture
  8. 4.8Battery management
  9. 4.9Image quality
  10. 4.10Practical exercise: multispectral flight plan for 50 ha
  1. 5.1Generation of multispectral orthomosaics
  2. 5.2Digital surface models
  3. 5.3Georeferencing with ground control points
  4. 5.4RTK and PPK
  5. 5.5Coordinate systems
  6. 5.6Geometric accuracy of products
  7. 5.7Point clouds
  8. 5.8Plant height
  9. 5.9Canopy volume
  10. 5.10Lab: orthomosaic with ground control points
  1. 6.1Vigour and biomass
  2. 6.2Water stress detection
  3. 6.3Nitrogen nutrition status
  4. 6.4Pests and diseases
  5. 6.5Weed detection
  6. 6.6Yield estimation
  7. 6.7Plant counting
  8. 6.8High-throughput phenotyping
  9. 6.9Field validation
  10. 6.10Case study: water stress detection in an olive grove
  1. 7.1Supervised crop classification
  2. 7.2Semantic image segmentation
  3. 7.3Object detection
  4. 7.4Random forest models for yield
  5. 7.5Convolutional networks on agricultural images
  6. 7.6Time series analysis
  7. 7.7Training data
  8. 7.8Model validation
  9. 7.9Cloud platforms
  10. 7.10Practical exercise: weed segmentation with neural networks
  1. 8.1Field zoning
  2. 8.2Variable-rate application maps
  3. 8.3Variable-rate nitrogen fertilisation
  4. 8.4Variable-rate seeding
  5. 8.5Variable deficit irrigation
  6. 8.6ISOBUS and shapefile formats
  7. 8.7Integration with machinery
  8. 8.8Agronomic validation of prescriptions
  9. 8.9Economic return of variable-rate application
  10. 8.10Workshop: nitrogen prescription map
  1. 9.1Fundamentals of aerial spraying
  2. 9.2Drift and droplet size
  3. 9.3Nozzle and flow calibration
  4. 9.4Plant protection product regulations
  5. 9.5Spot applications on patches
  6. 9.6Safety of the operator and surroundings
  7. 9.7Operational performance per hectare
  8. 9.8Comparison with ground machinery
  9. 9.9International experience in Asia
  10. 9.10Case study: drift analysis of a spraying drone
  1. 10.1Geographic information systems
  2. 10.2QGIS for agriculture
  3. 10.3Spatial databases
  4. 10.4Interoperability of agricultural data
  5. 10.5Spatial analysis of variability
  6. 10.6Thematic mapping of plots
  7. 10.7Open data from Copernicus and SIGPAC
  8. 10.8Digital farm record book
  9. 10.9Privacy of agricultural data
  10. 10.10Practical exercise: spatial analysis in QGIS
  1. 11.1European UAS regulation
  2. 11.2Regulations on plant protection product application
  3. 11.3Common Agricultural Policy
  4. 11.4Farm sustainability
  5. 11.5Carbon footprint
  6. 11.6Regenerative agriculture and soil
  7. 11.7Biodiversity in agricultural environments
  8. 11.8Data-based agricultural insurance
  9. 11.9Legal liability in aerial applications
  10. 11.10Case study: CAP eco-schemes with drone data
  1. 12.1Definition of the farm
  2. 12.2Identification of the agronomic problem
  3. 12.3Capture design
  4. 12.4Data processing
  5. 12.5Analysis and indices
  6. 12.6Prescription map
  7. 12.7Economic evaluation of the solution
  8. 12.8Implementation plan
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

The programme is delivered 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so you can combine it with your job.

Each module combines theory, guided reading of the original regulations and applied exercises. The working software is Pix4Dfields, Agisoft Metashape, QGIS, DJI Terra and Python, in free, academic or evaluation versions. Exercises are based on real procedures: multispectral flights over crops, preparation of prescription maps and field validation of indices. Assessment is continuous for each module, plus the final Master's Thesis.

Standards covered: Implementing Regulation (EU) 2019/947, Directive 2009/128/EC on the sustainable use of pesticides, Royal Decree 1311/2012, Regulation (EU) 2021/2115 (CAP), ISO 11783 ISOBUS, ISO 19115, Royal Decree 1054/2022 (digital farm record book).

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Regulation (EU) 2019/947, Directive 2009/128/EC, the CAP Regulation (EU) 2021/2115 and ISO 11783 applied to real cases, not as a theoretical annex.
Industry software:Pix4Dfields, Agisoft Metashape, QGIS, DJI Terra and Python, in free, academic or evaluation versions.
Master's Thesis with defence:A precision agriculture solution for a real farm, from capture design to prescription map and economic evaluation, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Multispectral flight plan
Plan a multispectral flight over 50 ha with calibration panels and ground control.
Orthomosaic and indices
Produce a georeferenced orthomosaic and calculate vegetation indices.
Crop stress and weed analysis
Detect water stress and segment weeds using AI.
Nitrogen prescription map
Build a variable-rate nitrogen prescription map in ISOBUS format.
Master's Thesis
Design and evaluate a precision agriculture solution for a real farm, defended before a panel.
Career opportunities

Where this takes you

Precision agronomistRemote sensing technicianGeospatial analystAgricultural drone pilotAgritech consultantFarm manager
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Pix4Dfields, Agisoft Metashape, QGIS, DJI Terra and Python, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software, and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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