GIS and Remote Sensing for Precision Agriculture Training Course

Course overview

Farm teams increasingly receive satellite scenes, drone imagery, yield monitor files, soil test results, machinery tracks and weather feeds, yet often lack a repeatable way to convert these inputs into field-level decisions. Poorly aligned layers, unmanaged coordinate systems, cloud-contaminated imagery and unvalidated vegetation indices can produce prescription maps that waste seed, fertiliser, water and scouting time. This course addresses the operational gap between agricultural data collection and defensible precision-management actions.

Participants learn to build and manage agricultural geodatabases; prepare field boundaries, management zones and sampling points; process multispectral satellite and UAV imagery; calculate NDVI, NDRE and related indices; classify crop conditions; and integrate imagery with yield, soil, elevation and weather data. They apply spatial analysis to identify within-field variability, prioritise scouting, design soil-sampling grids, assess irrigation performance and create variable-rate application prescriptions. The course uses QGIS, ArcGIS Pro, Google Earth Engine and GDAL workflows, with attention to data quality, metadata, coordinate reference systems and agronomic interpretation.

Instructor-led demonstrations are followed by guided laboratory work using realistic farm datasets from row-crop and irrigated production systems. Teams investigate a season-long field scenario, moving from raw imagery and machine data to actionable zones and maps. Each participant leaves with a documented precision-agriculture project pack: a GIS project, processed imagery layers, field-condition analysis, management-zone rationale, sampling or scouting plan, variable-rate prescription concept and an implementation plan for their own operation.

The programme is suited to agricultural professionals who already work with farm records, spatial data or crop-monitoring information and need to lead or improve GIS-enabled decision making across farms, estates, advisory services or agri-food supply chains.

Course objectives

By the end of this course, participants will be able to:

• Build a farm geodatabase containing field boundaries, crop history, soil results, yield records and operational metadata
• Prepare satellite, UAV and machinery datasets using coordinate reference systems, raster alignment and quality-control checks
• Calculate NDVI, NDRE and vegetation-index change layers to detect crop vigour variability
• Classify imagery and delineate management zones using supervised classification, clustering and field knowledge
• Design targeted scouting routes and soil-sampling plans from spatial variability and risk layers
• Integrate yield maps, terrain derivatives, soil attributes and weather observations to diagnose limiting factors
• Create variable-rate seed, fertiliser or irrigation prescription-map concepts with traceable zone logic
• Produce a decision-ready precision-agriculture map pack and implementation plan for a selected field or farm

Benefits of attending

Target competencies

Who should attend

• Precision Agriculture Managers — who must turn farm data into repeatable management-zone and prescription decisions
• Agronomists and Crop Consultants — who need to target scouting, sampling and recommendations at within-field variability
• Farm Managers and Estate Managers — who oversee input budgets, machinery operations and field-performance improvement
• GIS Analysts in Agriculture — who need domain-specific workflows for imagery, yield data and agronomic layers
• Irrigation Managers — who need spatial evidence to identify water-stress patterns and prioritise system inspections
• Agricultural Research Officers — who analyse field trials, crop-condition imagery and site-specific production data

Requirements and prerequisites

Participants should be comfortable using spreadsheets and working with tabular farm records such as soil tests, yield exports or crop histories. Prior exposure to GIS concepts—layers, attributes, points, lines, polygons and map projections—is expected, as is the ability to navigate a desktop mapping application such as QGIS or ArcGIS Pro. Familiarity with basic agronomy, crop growth stages and common farm inputs will help participants interpret results. Programming, advanced statistics, drone-pilot certification and prior Google Earth Engine experience are not required; Earth Engine scripts are introduced through guided examples.

Training methodology

The course combines short instructor-led technical briefings with daily GIS laboratories using agricultural field boundaries, multispectral imagery, yield exports, soil-test points, terrain models and weather records. The instructor demonstrates each workflow in QGIS, ArcGIS Pro, Google Earth Engine or GDAL before participants reproduce and adapt it. Case discussions test the agronomic meaning and limitations of mapped patterns, while group reviews compare alternative zone and prescription decisions. The final sessions use an application-planning workshop to translate each participant’s analysis into a workable farm, estate or advisory-service process.

Course outline

Day 1: Precision agriculture GIS foundations

• Precision-agriculture decision cycle and data-to-action workflow
• Agricultural spatial data models for fields, blocks and management units
• Vector geometry, raster cells and attribute-table structures
• Coordinate reference systems, datums and agricultural map accuracy
• Farm boundary digitising and topology validation
• Field identifiers, crop-history schemas and metadata conventions
• QGIS and ArcGIS Pro project setup for farm datasets

Workshop: Participants build a structured farm GIS project from supplied field boundaries, crop records and operational reference layers.

Day 2: Farm data preparation and quality control

• Importing CSV, shapefile, GeoPackage and geodatabase datasets
• Geocoding soil-sampling and farm-observation records
• Cleaning yield-monitor exports and removing implausible values
• Raster and vector reprojection for multi-source alignment
• Spatial joins, field clipping and attribute normalisation
• Positional accuracy, temporal matching and missing-data assessment
• GDAL conversion, mosaicking and raster-inspection commands

Workshop: Participants audit and prepare a mixed farm dataset, producing a quality-control log and analysis-ready geodatabase.

Day 3: Satellite and UAV imagery for crop monitoring

• Sentinel-2, Landsat and commercial imagery selection criteria
• UAV multispectral imagery, orthomosaics and ground-control considerations
• Spatial, spectral and temporal resolution trade-offs
• Cloud masking, shadow detection and scene-quality screening
• Reflectance products and atmospheric-correction concepts
• Band combinations for crop, soil and water interpretation
• Google Earth Engine image collections and filtering workflows

Workshop: Participants select and prepare a cloud-free image time series for a crop-monitoring window in Google Earth Engine.

Day 4: Vegetation indices and crop-condition analysis

• NDVI calculation and interpretation limits
• NDRE for canopy chlorophyll and later-season assessment
• SAVI and soil-background adjustment in sparse canopies
• NDWI and moisture-related vegetation signals
• Raster algebra and index scaling in desktop GIS
• Multi-date index differencing and seasonal trend analysis
• Ground-truthing index patterns with field observations

Workshop: Participants create an index comparison map and identify priority crop-condition anomalies for field verification.

Day 5: Management zones and targeted field investigation

• Sources of within-field variability and stable-zone concepts
• Unsupervised clustering for management-zone candidates
• Supervised classification using agronomic training samples
• Terrain derivatives including slope, aspect and topographic wetness
• Zone boundary smoothing and minimum-operable-area rules
• Scouting-route optimisation from anomaly and accessibility layers
• Grid, zone and directed soil-sampling designs

Workshop: Participants delineate preliminary management zones and produce a targeted scouting and soil-sampling map.

Day 6: Yield, soil and terrain integration

• Yield-map calibration, lag correction and header-width issues
• Yield-data filtering and spatial aggregation methods
• Interpolation of soil-test points using IDW and kriging concepts
• Digital elevation models and drainage-pattern analysis
• Overlay analysis of yield, soil texture and topography
• Correlation, confounding and causation in field-performance diagnosis
• Temporal stability analysis across multiple harvest seasons

Workshop: Participants diagnose likely yield-limiting factors by integrating cleaned yield maps, soil results and terrain layers.

Day 7: Irrigation, water stress and weather intelligence

• Spatial indicators of crop water stress
• Irrigation-zone mapping for pivots, drip systems and blocks
• Evapotranspiration and crop-coefficient data concepts
• Rainfall interpolation and on-farm weather-station integration
• Thermal imagery applications and interpretation cautions
• Detecting irrigation non-uniformity from imagery patterns
• Water-risk prioritisation and field-inspection workflows

Workshop: Participants create an irrigation inspection-priority map combining vegetation indices, weather records and system boundaries.

Day 8: Variable-rate prescriptions and operational mapping

• Prescription-map logic for seed, nutrient and irrigation decisions
• Rate tables, zone coding and equipment-controller requirements
• Converting management zones into operational polygons
• Minimum mapping units and machinery pass-width constraints
• Buffering, headlands and exclusion-area treatment
• Prescription validation against agronomic and budget constraints
• Export formats, data transfer and version-control practices

Workshop: Participants develop a variable-rate fertiliser prescription concept with zone rates, operational constraints and an export-ready map.

Day 9: Validation, reporting and decision governance

• Ground-truth protocols for imagery-derived recommendations
• Accuracy assessment using confusion matrices and reference samples
• Field-trial design for testing zone and rate decisions
• Uncertainty communication in crop-condition maps
• Map layout design for farm managers and operators
• Dashboards, map books and seasonal reporting structures
• Data governance, ownership and sharing with advisers and contractors

Workshop: Participants validate a classified crop-condition map and produce a manager-ready map layout with findings and caveats.

Day 10: Integrated precision-agriculture project

• Defining a field-level decision question and success measures
• Selecting fit-for-purpose imagery and supporting datasets
• Constructing an end-to-end GIS analysis workflow
• Comparing alternative management-zone scenarios
• Estimating operational benefits and implementation constraints
• Presenting evidence, assumptions and recommendations
• Ninety-day adoption plan for farm or advisory workflows

Workshop: Participants complete and present a precision-agriculture project pack containing analysis maps, management zones, recommended actions and a 90-day implementation plan.

Tools & standards covered

QGIS, ArcGIS Pro, Google Earth Engine, GDAL

A typical training day

08:30 – 10:30	First session
10:30 – 10:45	Refreshment break
10:45 – 12:30	Second session
12:30 – 13:30	Lunch and networking
13:30 – 15:00	Third session
15:00 – 15:15	Refreshment break
15:15 – 16:30	Workshop and daily review

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions