GIS for Smart Agriculture Training Course

Participants can design an agricultural geodatabase with consistent identifiers, attributes and relationships.

• Feature classes for fields, crop blocks, irrigation units and infrastructure
• Unique field identifiers connecting spatial boundaries with farm-management records
• Attribute domains controlling crop varieties, growth stages and land-use codes
• Relational tables for planting, inputs, scouting, harvest and soil observations
• Metadata records documenting source, date, accuracy and data custodians
• Schema design supporting seasonal analysis without duplicating field geometry

Participants can create and validate field boundaries suitable for image analysis and farm reporting.

• Digitising field polygons from basemaps, imagery and GNSS tracks
• Topology rules detecting gaps, overlaps, slivers and duplicate boundaries
• Boundary editing guided by headlands, canals, fences and crop rows
• Area calculations checked against farm records and legal parcel data
• Geometry validation before zonal statistics and prescription-map production
• Version control procedures for changing fields, leases and crop blocks

Participants can plan GNSS-supported field observations that validate imagery and support crop diagnosis.

• GNSS accuracy classes for field boundaries, sample points and assets
• Sampling forms capturing crop stage, pest symptoms and management history
• Point, line and polygon observations matched to agricultural inspection needs
• Photographs linked to coordinates, timestamps and structured observation codes
• Offline mobile collection workflows for remote farms and weak connectivity
• Quality checks for duplicate records, misplaced points and missing attributes

Participants can select suitable satellite data based on crop, timing, resolution and cloud conditions.

• Sentinel-2 bands, revisit intervals and 10-metre agricultural applications
• Landsat surface reflectance for seasonal analysis and historical comparison
• Spatial, spectral and temporal resolution trade-offs for farm questions
• Cloud, haze and shadow effects on optical crop monitoring
• Scene selection using phenology, planting dates and irrigation schedules
• Surface reflectance products distinguished from visual satellite basemaps

Participants can prepare imagery for consistent field-level analysis using documented pre-processing steps.

• Projecting imagery into the farm coordinate reference system
• Clipping scenes to farm boundaries and analysis areas
• Cloud masking with Sentinel-2 Scene Classification Layer outputs
• Band stacking procedures for multispectral index calculation
• Resampling choices and their effects on field-scale interpretation
• Temporal compositing methods reducing cloud contamination in seasonal series

Participants can calculate and interpret vegetation indices for crop vigour, cover and stress screening.

• NDVI calculation from near-infrared and red reflectance bands
• NDRE applications for chlorophyll assessment in later crop stages
• SAVI adjustments for sparse vegetation and exposed soil conditions
• Index interpretation against crop phenology, cultivar and canopy structure
• Field-level zonal statistics summarising mean, range and variability
• False-colour composites used to inspect unusual vegetation-index patterns

Participants can produce and assess supervised land-cover classifications for agricultural monitoring.

• Training samples representing crops, bare soil, water and built features
• Random Forest classification using spectral bands and derived indices
• Class definitions aligned with agricultural reporting and monitoring requirements
• Confusion matrices reporting producer, user and overall accuracy
• Independent validation samples separated from classifier training data
• Post-classification filtering removing isolated pixels and implausible fragments

Participants can analyse multi-date imagery to identify crop-development patterns and anomalies.

• Seasonal NDVI profiles linked to planting, growth and harvest dates
• Time-series charts comparing fields, varieties and management treatments
• Anomaly detection against historical median vegetation-index trajectories
• Phenology metrics estimating green-up, peak growth and senescence timing
• Change detection separating genuine crop signals from acquisition differences
• Alert thresholds prioritising fields for scouting and agronomic inspection

Participants can integrate soil observations and laboratory results into field variability analysis.

• Soil sampling grids, management zones and targeted sampling strategies
• Laboratory attributes for pH, organic carbon, nutrients and texture
• Interpolation methods including inverse distance weighting and ordinary kriging
• Semivariogram concepts for assessing spatial dependence in soil properties
• Soil maps combined with crop indices and yield observations
• Sampling-density decisions based on variability, cost and management purpose

Participants can derive terrain indicators that inform drainage, erosion and operational planning.

• Digital elevation models assessed for resolution, source and vertical accuracy
• Slope, aspect and curvature derived for field-condition analysis
• Flow direction and accumulation modelling surface-water movement
• Topographic wetness index identifying potential water accumulation zones
• Depression mapping for drainage assessment and field-access planning
• USLE factor inputs prepared from terrain, soil and cover datasets

Participants can use spatial indicators to identify irrigation variability and water-management priorities.

• Irrigation zones mapped from pivots, canals, valves and field blocks
• Crop water demand interpreted using reference evapotranspiration and crop coefficients
• Thermal imagery principles for screening canopy-temperature differences
• Vegetation-index patterns compared across irrigation units and irrigation events
• Water productivity indicators relating biomass proxies to applied water
• Priority maps identifying suspected under-irrigation, over-irrigation and drainage issues

Participants can specify UAV data products and integrate them appropriately with farm GIS datasets.

• UAV mission parameters affecting ground sample distance and image overlap
• Orthomosaic, digital surface model and point-cloud agricultural outputs
• Ground control points supporting positional accuracy and repeat surveys
• Multispectral drone sensors compared with standard RGB camera systems
• Canopy gaps, weed patches and drainage features interpreted from UAV imagery
• UAV data limitations including wind, shadows, regulations and processing time

Participants can clean yield-monitor data and relate harvest variation to spatial drivers.

• Yield monitor attributes including mass flow, moisture and travel speed
• Cleaning rules for header delays, overlaps and implausible yield values
• Spatial aggregation methods creating stable yield surfaces from point records
• Multi-year yield patterns distinguished from single-season anomalies
• Yield maps compared with soil, terrain and vegetation-index layers
• Harvest-data uncertainty documented before management recommendations are made

Participants can create management zones from multiple spatial datasets and operational constraints.

• Zone objectives defined for fertiliser, seeding, irrigation or scouting
• Standardisation of soil, yield, terrain and vegetation input layers
• Clustering methods including k-means for field-zone delineation
• Zone boundaries refined using machinery widths and field-operational constraints
• Minimum mapping units preventing impractical fragmented treatment areas
• Agronomic review linking zones to plausible soil and crop processes

Participants can prepare prescription-ready spatial data for variable-rate agricultural operations.

• Prescription maps linking management zones with recommended application rates
• Rate tables constrained by agronomic targets, budgets and equipment limits
• Fertiliser recommendations checked against nutrient balances and soil-test results
• Seeding-rate zones aligned with yield potential and establishment risk
• Irrigation prescriptions expressed through zones, durations and flow requirements
• Export validation for machinery formats, units, attributes and coordinate systems

Participants can convert GIS findings into prioritised scouting plans and field action records.

• Scouting priorities ranked using anomalies, crop stage and economic risk
• Route planning for efficient inspection of dispersed field locations
• Observation protocols distinguishing nutrient deficiency, disease and water stress
• Decision trees connecting imagery symptoms with field-verification actions
• Follow-up records documenting intervention, timing and observed response
• Feedback loops updating map interpretation from confirmed field conditions

Participants can establish practical controls for agricultural spatial data sharing, quality and custody.

• Data ownership clauses for farm records, imagery and contractor outputs
• FAIR principles applied to agricultural datasets and metadata records
• ISO 19115 metadata elements for geospatial dataset documentation
• Access controls separating farm, contractor, adviser and programme permissions
• Data-quality dimensions covering completeness, positional accuracy and timeliness
• Interoperability checks between GIS, farm-management and machinery systems

Participants can communicate agricultural GIS findings through clear maps, dashboards and decision briefings.

• Map layouts designed for farm managers, agronomists and field teams
• Legends, classifications and colour ramps supporting accurate interpretation
• Area, yield and input summaries calculated from mapped zones
• Uncertainty statements explaining imagery limits, assumptions and validation status
• Dashboard indicators tracking crop condition, irrigation and scouting priorities
• Decision briefings linking spatial findings with actions, owners and dates

Participants can build, review and present a complete smart agriculture GIS project containing validated data, analysis outputs, management recommendations and implementation controls.

• Problem statement defining crop, field, decision and expected operational value
• Geodatabase assembled from boundaries, imagery, observations and supporting records
• Analysis workflow combining indices, terrain, soil and management data
• Management zones or priority maps produced with documented assumptions
• Peer review tests spatial accuracy, agronomic logic and operational feasibility
• Final presentation communicates recommendations, limitations, responsibilities and next actions