01 · Data capture
UAV multispectral acquisition
Plan and capture overlapping imagery over the defined farm boundary, recording flight conditions, crop stage and sensor settings.
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Precision Agriculture · UAV Mapping
Multispectral Survey for Crop Health
Using UAV multispectral imagery and geospatial analysis to visualise crop variability, prioritise field scouting and support evidence-led agricultural decisions across Kenya and Africa.
AI-generated illustrative visual for concept and marketing use; it is not a record of a verified site survey or measured crop-health result.
Survey approach
From aerial capture to crop-health insight
Multispectral sensors record reflected energy in selected wavelength bands. When imagery is properly calibrated and processed, the resulting maps can reveal spatial differences that may not be clear in standard RGB imagery.
02 · Field operations
Flight monitoring and coverage
Monitor flight progress, image coverage and field conditions to support a consistent dataset for processing.
03 · Calibration
Radiometric preparation
Apply the sensor-recommended calibration workflow, including reflectance-panel measurements where required.
04 · Analysis
Vegetation-index mapping
Generate and review suitable vegetation indices, then identify areas that may benefit from ground verification.
Why multispectral?
Understand variation across the field
Index maps are useful for highlighting patterns and helping teams decide where to inspect. They are indicators of vegetation response—not a diagnosis of a specific crop problem.
- Map within-field vegetation variability.
- Prioritise field visits using high-, medium- and low-index zones.
- Compare repeat surveys when acquisition and processing are consistent.
- Combine imagery with crop-stage context and ground observations.
Interpretation matters: water stress, nutrient limitations, pest or disease pressure, soil exposure, drainage and image artefacts can produce similar patterns. Ground checks are needed before acting.
Vegetation indices
Index outputs
NDVI
Green vegetation activity
(NIR − Red) / (NIR + Red). A relative indicator of vegetation activity and spatial variability.
NDRE
Red-edge response
(NIR − Red Edge) / (NIR + Red Edge). Can help assess variability in denser canopies.
SAVI
Soil-adjusted vegetation
((NIR − Red) / (NIR + Red + L)) × (1 + L). Helps reduce soil-brightness influence when appropriate.
Processing methodology
A structured workflow for usable geospatial outputs
Each stage should be documented and quality-checked before outputs are used for crop-management decisions.
1
Plan
Confirm farm boundary, crop stage, permissions, flight plan and survey requirements.
2
Capture
Acquire overlapping multispectral imagery under suitable and documented conditions.
3
Process & check
Calibrate, align bands, generate orthomosaics and check coverage and georeferencing.
4
Interpret
Produce index maps, summarise patterns and recommend locations for field verification.
Project outputs
Potential deliverables
Final formats and available layers are confirmed for each project and depend on the sensor and processing workflow.
Georeferenced RGB orthomosaic
Calibrated multispectral band orthomosaics
NDVI raster and map
NDRE and SAVI rasters, where supported
Field boundary and priority zones, where requested
PDF report with maps, methods and limitations
Project-specific results: Farm name, location, survey date, index statistics, mapped area and findings should be populated only after validated survey data are available.
Planning a crop-health survey?
Share your farm location, crop type, area and intended use. Skymap Africa can help define a suitable multispectral acquisition and mapping workflow.