General Tools

Field Trafficability And Operation Planning

Function name: field_trafficability_and_operation_planning

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Field Trafficability and Operation Planning

Problem It Solves

Where can equipment operate safely now, and which areas should be delayed or rerouted?

Who It Is For

Machinery planning teams, farm managers, and field operations coordinators.

Primary User

Farm operations teams and precision agriculture service providers.

What It Does

Scores field trafficability and operation timing risk from terrain and saturation context.
Produces operation classes for go/hold/reroute-style field execution decisions.

How It Works

Derives slope from DEM and harmonizes moisture/rainfall context to the same grid.
Blends terrain, soil saturation, and rainfall-risk penalties into trafficability scores.
Converts continuous scores into operation classes for practical planning use.
QA acceptance guidance:
status=pass indicates low-trafficability burden stayed within baseline tolerance.
diagnostics.acceptance_thresholds.low_trafficability_fraction_review defines review escalation threshold.
High summary.low_trafficability_fraction should trigger cautious equipment scheduling and field confirmation.
MVP hardening assets:
Benchmark scaffold: tests/fixtures/precision_ag_ops_benchmark/
Promotion guide: docs/internal/development/TERRAIN_PRECISION_AG_BENCHMARK_PROMOTION_GUIDE_2026_04_14.md

ParameterOptionalDescription demnoDEM raster for terrain slope context. soil_moisturenoSoil saturation/moisture raster normalized to [0,1]. rainfall_forecastyesOptional rainfall-risk raster normalized to [0,1].

Outputs

ParameterTypeDescription trafficability_scoreGeoTIFFContinuous trafficability score [0,1] (higher is better). operation_classGeoTIFFDiscrete operation class raster (1 favorable to 4 poor). summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

traffic, op_class, summary = wbe.field_trafficability_and_operation_planning( dem="data/dem.tif", soil_moisture="data/soil_saturation.tif", rainfall_forecast="data/rain_risk.tif", output_prefix="output/field_trafficability", )

print(traffic) print(op_class) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

In Season Crop Stress Intervention Planning

Function name: in_season_crop_stress_intervention_planning

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

In-Season Crop Stress Intervention Planning

Problem It Solves

Where should interventions be prioritized this week to limit stress-driven yield loss?

Who It Is For

Agronomy advisors and farm operations teams managing in-season response.

Primary User

Precision agronomy service providers and large production farms.

What It Does

Prioritizes intervention zones from in-season crop stress indicators.
Combines vigor decline with optional thermal and moisture stress context.

How It Works

Uses NDVI/vigor deficit as the primary stress signal.
Harmonizes optional canopy-temperature and soil-moisture rasters to the NDVI grid.
Produces intervention-priority and intervention-class surfaces.
QA acceptance guidance:
status=pass indicates intervention burden is below review threshold.
diagnostics.acceptance_thresholds.high_priority_fraction_review defines escalation threshold for broad intervention risk.
High summary.high_priority_fraction should trigger stress-source verification before large-scale treatment rollout.
MVP hardening assets:
Benchmark scaffold: tests/fixtures/precision_ag_ops_benchmark/
Promotion guide: docs/internal/development/TERRAIN_PRECISION_AG_BENCHMARK_PROMOTION_GUIDE_2026_04_14.md

Inputs

ParameterOptionalDescription ndvinoNDVI/vigor raster normalized to [0,1]. canopy_temperatureyesOptional thermal-stress raster normalized to [0,1]. soil_moistureyesOptional moisture-deficit raster normalized to [0,1].

Outputs

ParameterTypeDescription intervention_priorityGeoTIFFContinuous intervention-priority score [0,1]. intervention_classGeoTIFFDiscrete intervention class raster (1 low to 4 urgent). summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

priority, classes, summary = wbe.in_season_crop_stress_intervention_planning( ndvi="data/ndvi_current.tif", canopy_temperature="data/canopy_temp_stress.tif", soil_moisture="data/moisture_deficit.tif", output_prefix="output/in_season_stress", )

print(priority) print(classes) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Precision Irrigation Optimization

Function name: precision_irrigation_optimization

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Precision Irrigation Optimization

Problem It Solves

Where should irrigation depth be increased or reduced to achieve target moisture with less water waste?

Who It Is For

Precision agriculture teams, irrigation planners, and agronomy analytics groups.

Primary User

Large growers, precision ag service providers, and irrigation technology partners.

What It Does

Generates variable-rate irrigation prescription depth from terrain and moisture context.
Estimates moisture stress risk to prioritize intervention zones.
Emits summary diagnostics suitable for irrigation planning dashboards.

How It Works

Estimates local moisture deficit from target_moisture and measured or terrain-inferred moisture.
Adjusts prescription depth by slope-derived terrain factor and max_irrigation_mm limits.
Converts prescribed depth to VRI zones and computes moisture-stress risk index.
Indicative formula: prescribed_mm = max(0, target - moisture) * max_irrigation_mm * terrain_factor.

Why It Wins

Produces direct raster-ready prescription depths and risk diagnostics from reproducible inputs.

Typical Buying Trigger

A grower or consultant needs to shift from uniform irrigation to variable-rate execution.

Typical Presets

fast for quick field-scale recommendations.
balanced for default VRI planning.
conservative for stronger terrain-risk penalties.

Inputs

ParameterOptionalDescription demnoDigital elevation model used as the terrain reference surface. optional soil_moistureyesOptional soil moisture raster used to refine irrigation need and stress scoring. profile: fast | balanced | conservativenoProcessing profile controlling sensitivity, quality strictness, and runtime tradeoffs. target_moisturenoTarget moisture level used to compute irrigation prescription amounts. max_irrigation_mmnoMaximum irrigation depth allowed in generated irrigation recommendations.

Outputs

ParameterTypeDescription irrigation_prescriptionGeoTIFFIrrigation prescription raster indicating recommended application depth. moisture_stress_riskGeoTIFFMoisture stress risk raster supporting irrigation prioritization. vri_zonesGeoTIFFVariable-rate irrigation zone raster. summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

When sweep_spec is supplied, the workflow also emits run_matrix_summary, sensitivity_report, sensitivity_report_html, and stability_map. The sensitivity report includes metrics.primary_metric, metrics.primary_relative_span, and metrics.stability_class (high, medium, low), while stability_map uses classes 3=high, 2=medium, 1=low.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

prescription, stress, zones, summary = wbe.precision_irrigation_optimization( dem="data/dem.tif", soil_moisture="data/soil_moisture.tif", profile="balanced", target_moisture=0.6, max_irrigation_mm=18.0, output_prefix="output/irrigation", )

print(prescription) print(stress) print(zones) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Precision Ag Yield Zone Intelligence

Function name: precision_ag_yield_zone_intelligence

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Precision Ag Yield Zone Analysis

Problem It Solves

Which management zones should receive differentiated input strategy based on stable productivity patterns?

Who It Is For

Precision agronomy teams and farm analytics providers.

Primary User

Enterprise farms, agronomy consultancies, and digital ag platforms.

What It Does

Builds yield-stability surfaces from yield productivity context.
Integrates optional terrain context to improve zone differentiation.
Produces management-zone rasters for variable-rate strategy design.

How It Works

Normalizes yield response and computes local stability/variability signatures.
Optionally blends terrain_context influence with profile-dependent weighting.
Partitions pixels into zone_count management classes and emits zone confidence and polygons.
Indicative formula: zone_score ~= w_yyield_stability + w_tterrain_term; zone = discretize(zone_score, zone_count).

Why It Wins

Converts yield and terrain context into explicit, contract-backed management zone outputs.

Typical Buying Trigger

A farm program is formalizing zone-based management for seed, fertility, and water decisions.

Typical Presets

fast for high-throughput zone generation.
balanced for default zone planning.
conservative for stronger terrain-context influence.

Inputs

ParameterOptionalDescription yield_surfacenoYield raster used to estimate productivity stability and zone segmentation. optional terrain_contextyesOptional terrain context raster used to strengthen management-zone differentiation. profile: fast | balanced | conservativenoProcessing profile controlling sensitivity, quality strictness, and runtime tradeoffs. zone_count (2..8)noRequested number of management zones to generate.

Outputs

ParameterTypeDescription yield_stabilityGeoTIFFYield stability raster used to delineate management zones. management_zonesGeoTIFFManagement-zone classification raster. management_zones_vectorGeoPackageVectorized management zones for operations and reporting. zone_confidenceGeoTIFFConfidence surface associated with generated management zones. summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

stability, zones, zone_polys, confidence, summary = wbe.precision_ag_yield_zone_intelligence( yield_surface="data/yield_surface.tif", terrain_context="data/terrain_context.tif", profile="balanced", zone_count=4, max_zone_features=5000, output_prefix="output/yield_zone", )

print(stability) print(zones) print(zone_polys) print(confidence) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Soil Landscape Classification

Function name: soil_landscape_classification

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Soil Landscape Classification

Problem It Solves

Which terrain-defined landform units dominate the area, and how can they guide management zoning or sampling strategy?

Who It Is For

Geomorphometry analysts, soil-landscape researchers, and precision-ag planning teams.

Primary User

Agronomy/land capability groups, environmental consulting teams, and land resource agencies.

What It Does

Classifies landform units using multiscale curvature and slope signatures.
Produces raster class maps and optional polygon outputs.
Emits summary distributions for each landform class.

How It Works

Computes slope, profile curvature, and plan curvature at fine and coarse scales.
Applies rule-based landform assignment thresholds to each pixel.
Optionally polygonizes class regions and aggregates class-area summary statistics.
Indicative formula: landform_class = rule(slope, k_profile, k_plan, multiscale_signature).

Why It Wins

Couples interpretable geomorphometric classes with optional polygon outputs for direct planning integration.

Typical Buying Trigger

A land management or agronomy program needs terrain-driven zones for sampling design or variable-rate planning.

Typical Presets

default thresholds for general terrain partitioning.
tune fine/coarse scales for local vs regional landform emphasis.

Inputs

ParameterOptionalDescription input DEMnoInput DEM used for terrain-derivative and landform classification workflows. flat/profile/plan thresholdsnoCurvature and surface-form thresholds used to separate landform classes. fine_scale, coarse_scalenoMultiscale analysis windows used to capture local and broad terrain structure. optional landform_polygons_outputyesOptional switch/path to emit vectorized landform polygons.

Outputs

ParameterTypeDescription landform_unitsGeoTIFFCategorical landform unit raster from terrain analysis. multiscale_signatureGeoTIFFMultiscale terrain-signature raster for landscape interpretation. landform_polygonsoptional vectorOptional vectorized landform polygons for cartographic/reporting use. summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

landform, signature, polygons, summary = wbe.soil_landscape_classification( input="data/dem.tif", fine_scale=2.0, coarse_scale=8.0, output_prefix="output/soil_landscape", landform_polygons_output="output/landforms.gpkg", )

print(landform) print(signature) print(polygons) print(summary)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Yield Data Conditioning And QA

Function name: yield_data_conditioning_and_qa

PROProduction

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

Workflow Narrative

Yield Data Conditioning and QA

Problem It Solves

How do we turn raw, noisy harvest monitor points into defensible, analysis-ready yield products for management decisions?

Who It Is For

Precision-ag service providers, agronomy analytics teams, and farm data engineering groups.

Primary User

Enterprise farms, precision agriculture consultancies, and digital agronomy platforms.

What It Does

Runs an end-to-end conditioning pipeline for noisy combine-harvester yield points.
Orchestrates edge QA, pass reconstruction, optional multi-header reconciliation, filtering, normalization, and swath map generation.
Produces contract-ready summary outputs for auditability and downstream zoning workflows.

How It Works

Flags/removes likely edge points using local neighborhood support.
Optionally filters telemetry anomalies using speed and heading consistency thresholds.
Auto-resolves common monitor-export field aliases when requested names are missing.
Reconstructs pass structure from point geometry and heading continuity, then smooths yield using pass-aware neighborhood statistics.
Optionally applies distance-based lag correction along reconstructed passes to compensate harvest monitor delay.
Optionally converts raw yield to a target moisture basis using moisture_field_name and target_moisture_pct.
Optionally applies robust MAD-based clipping on filtered yield values before normalization.
Propagates a QA confidence score to final point outputs, then builds swath polygons for map-ready products.
Indicative formula: filtered_yield ~= weighted_neighborhood(yield) with outlier replacement when z = |y - mean_adjacent_pass| / sd_adjacent_pass exceeds threshold.

Why It Wins

Provides a reproducible and transparent preprocessing chain with explicit QA outputs rather than opaque one-click cleaning.

Typical Buying Trigger

A team needs to standardize inconsistent yield data cleaning before zone generation, variable-rate planning, or year-over-year analytics.

Typical Presets

fast: lighter cleanup with larger tolerances for rapid turnaround.
balanced: default production profile.
strict: stronger edge/outlier controls for high-confidence analytics.

Inputs

ParameterOptionalDescription input (yield point vector)noRaw yield telemetry points used as input to the conditioning and QA pipeline. yield_field_name, moisture_field_name, target_moisture_pct, header_field_namenoField-name mappings for yield/moisture/header attributes used by cleaning stages. use_field_aliasesnoWhether known attribute aliases should be resolved automatically. speed_field_name, heading_field_namenoTelemetry field mappings used for speed and heading quality checks. min_speed_kmh, max_speed_kmh, max_heading_change_degnoOperating-speed and heading-change limits used for telemetry QC filtering. profile: fast | balanced | strictnoOperational profile controlling sensitivity and QA strictness for risk workflows. swath_width, edge_radius, reconcile_radius, normalization_radiusnoGeometric parameters controlling swath-edge handling and neighborhood reconciliation. lag_correction_mode: none | distancenoLag correction mode controlling whether and how harvest lag compensation is applied. lag_distance_mnoDistance offset used when distance-based lag correction is enabled. filtering_mode: standard | robustnoOutlier-filtering method selection for yield cleaning. robust_mad_threshold, z_score_threshold, min_yield, max_yieldnoStatistical thresholds and hard limits used for yield outlier rejection. optional mean_tonnageyesOptional mean tonnage override used during normalization/reconciliation steps.

Outputs

ParameterTypeDescription qa_flagsGeoPackageQA flag layer identifying records that failed yield telemetry checks. telemetry_qc_pointsoptional GeoPackageOptional point layer of telemetry QC diagnostics by record. clean_pointsGeoPackageCleaned yield points after telemetry and statistical filtering. clean_mapGeoPackageCartography-ready cleaned yield layer for rapid map production. confidence_pointsGeoPackagePoint-level confidence diagnostics for cleaned yield records. pass_linesGeoPackageHarvest pass line features inferred from telemetry trajectories. pass_pointsGeoPackageHarvest pass points used in overlap and reconciliation analyses. lag_corrected_pointsoptional GeoPackageOptional points after lag-correction adjustment. moisture_adjusted_pointsoptional GeoPackageOptional points after moisture normalization adjustments. filtered_pointsGeoPackagePoints retained after standard filtering criteria are applied. robust_filtered_pointsoptional GeoPackageOptional points retained by robust filtering mode diagnostics. normalized_pointsGeoPackageYield points after normalization to common analytical basis. reconciled_pointsoptional GeoPackageOptional reconciled points after overlap and pass harmonization. summaryJSONMachine-readable summary report containing run metadata, QA diagnostics, and key metrics. html_reportHTMLHuman-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

Python Example

`import whitebox_workflows as wbw

wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

result = wbe.yield_data_conditioning_and_qa( input="data/yield_points.gpkg", yield_field_name="YIELD", header_field_name="HEADER", profile="balanced", swath_width=6.096, output_prefix="output/yield_pipeline", )

print(result)`

License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

Whitebox Workflows for QGIS User Manual