Satellite crop monitoring, vegetation index and weather-risk platform

Satellite crop monitoring, vegetation index and weather-risk platform

This profile defines a sourcing requirement for satellite crop and weather-risk intelligence platform. Suitable suppliers, origin, availability, and commercial terms are confirmed for the buyer’s project. The sourcing brief is structured around capacity, application, operating environment, required standards, destination, and delivery scope; the exact configuration requires supplier confirmation.

For farms, cooperatives, agronomy teams, contract-production businesses, insurers, banks, food companies, and software teams comparing satellite crop monitoring software in Türkiye, successful deployment depends on whether field boundaries can be onboarded reliably, which satellite sources and spectral indices are actually available, how clouds and image dates are handled, how weather and climate signals are converted into field alerts, how agronomic recommendations are governed, and whether data can move into an ERP, GIS, underwriting model, ESG workflow, or another agricultural application.

This configurable agricultural intelligence platform combines AI, satellite imagery, climate data, field-specific notifications, web and mobile access, multi-parcel B2B tools, data delivery, and APIs. Configurable analytics include NDVI, NDRE, SAVI, NDMI, NDWI, NDTI, residue indicators, weather forecasts, meteorological data, spraying-time guidance, elevation and slope mapping, storm and hail tracking, AI-assisted agronomic advice, and messaging workflows.

The platform is configured as an agricultural intelligence and climate-data stack combining satellite monitoring, field-risk analytics, agricultural weather, B2B portfolio software, APIs, and governed data delivery. Banking, insurance, ESG, carbon, procurement, and supply-chain workflows are scoped as project modules with their own data rights, validation, decision controls, and acceptance criteria.

Technical capability map

Technical layer	Technical capability	Project specification
Satellite crop monitoring	Multi-index crop monitoring with NDVI, NDRE, SAVI, NDMI, NDWI, NDTI, and project-specific residue or soil-cover layers.	Exact formula, spectral bands, imagery source, native and delivered resolution, cloud mask, acquisition date, compositing, scaling, valid range, no-data treatment, and archive depth.
Field intelligence	Field-level anomaly detection and comparison of satellite, weather, agronomic, and sensor data.	Boundary import, minimum polygon size, geometry validation, coordinate system, edge-pixel treatment, parcel statistics, thresholds, and traceability to image and processing version.
Weather and climate	Configurable 7-, 10-, or 15-day forecast horizons combined with observations and historical climate data.	Forecast model, issue frequency, grid size, lead time, archive, station or sensor assimilation, elevation, bias correction, missing data, and grid-versus-field interpretation.
Risk notifications	Field-specific disease, frost, drought, storm, hail, lightning, precipitation, and severe-weather alerts.	Crop and disease coverage, thresholds, inputs, latency, validation region, false-alert handling, channels, escalation, and field-observation closure.
Agronomic decision support	Spraying-window analysis, agronomic advisory workflows, conversational assistance, and personalized field recommendations.	Rule, statistical, machine-learning, or generative method; supported crops and languages; provenance, explanation, confidence, safety limits, model version, and audit trail.
Terrain context	DEM-derived elevation and slope layers for drainage, erosion, access, and field-operation planning.	DEM source, horizontal resolution, vertical datum, resampling, slope algorithm, unit, edge handling, and fitness for the intended decision.
B2B SaaS	Multi-organization monitoring of thousands of parcels, portfolio comparison, reporting, and historical archives.	Organization hierarchy, roles, field ownership, bulk onboarding, offboarding, audit logs, tenant separation, retention, training, and support.
Agricultural data API	APIs for field geometry, imagery, indices, weather, risk, alerts, ERP feeds, ESG, and enterprise analytics.	Base URL, endpoint catalogue, OpenAPI, versioning, authentication, quotas, asynchronous jobs, retries, webhooks, SDKs, SLA, and migration policy.
Data purchase and enterprise delivery	SaaS access, API integration, or scheduled bulk-data delivery.	Format, geography, period, refresh, source and derived-data rights, redistribution, retention, deletion, and processing terms.
Climate-risk and insurance context	Governed climate-risk analytics and parametric-trigger support for agricultural portfolios.	Trigger definition, source hierarchy, basis risk, reproducibility, outages, disputes, insurer responsibility, and the boundary between analytics and insurance delivery.

Understanding the named satellite indices

Satellite indices are best treated as screening, comparison, and prioritization layers. They can help a user decide where to inspect first, compare spatial patterns, and follow change over time. They do not, by themselves, establish the biological, chemical, hydraulic, or operational cause of every anomaly.

The formulas below describe common remote-sensing conventions for technical comparison. The contracted technical schedule defines the exact bands, constants, processing chain, scaling, cloud handling, sensor-specific treatment, output range, and version used for every operational layer.

Named layer	Common technical interpretation	Formula or implementation question
NDVI	Red and near-infrared vegetation index commonly used as a proxy for vegetation greenness and canopy activity.	Common form: (NIR - Red) / (NIR + Red). Confirm source bands, surface-reflectance product, scaling, cloud mask, and cross-sensor harmonization. Dense canopy can reduce sensitivity and soil background can affect low-cover fields.
NDRE	Red-edge and near-infrared index often used to examine canopy or chlorophyll-related variation, including later crop stages.	Common form: (NIR - RedEdge) / (NIR + RedEdge). Confirm which red-edge band is selected and whether values from different sensors are normalized before comparison.
SAVI	Soil-adjusted vegetation index intended to reduce some soil-background influence in lower-cover conditions.	Common form: ((NIR - Red) / (NIR + Red + L)) × (1 + L). Request the L value and whether a standard or modified implementation is used.
NDMI	Near-infrared and short-wave infrared index commonly used for vegetation or canopy moisture context.	A common form is (NIR - SWIR) / (NIR + SWIR). It is not a direct volumetric soil-moisture sensor reading; crop stage, canopy, exposed soil, and atmosphere can affect interpretation.
NDWI	A label used for more than one normalized-difference water-related formulation in remote sensing.	Ask for the exact bands and intended interpretation. Some workflows use Green/NIR for surface-water context; others use NIR/SWIR for vegetation-water context and may overlap conceptually with NDMI naming.
NDTI	Normalized-difference tillage-related index used in residue or tillage remote-sensing workflows.	Request the exact SWIR bands, formula, calibration, residue assumptions, crop and soil conditions, and validated operational use case.
Residue or soil-cover index	A project-specific spectral layer for residue, tillage, or soil-cover interpretation.	Define the exact SWIR or other bands, formula, calibration data, output range, crop and soil assumptions, validation method, and intended operational decision.

A buyer should also ask whether a displayed parcel value is the mean, median, percentile, weighted statistic, minimum/maximum, or another aggregation. A single parcel average can hide a small stress zone; a high-resolution raster can also show numerical variability that is not agronomically meaningful. For repeat monitoring, the record should preserve image date, time where available, sensor or source, cloud score, pixel size, processing version, index formula version, and the exact field geometry used.

Digital crop-scouting workflow. Photo by Mark Stebnicki via Pexels. Image source · Pexels License.

Satellite-source, cloud, and resolution questions

The imagery pipeline can combine Sentinel-class 10 m data with commercial high-resolution imagery around 3 m where field size or scouting precision requires it. Cloud-coverage filters, acquisition metadata, asynchronous premium-image ordering, job-status polling, and retry behavior are defined in the integration scope.

The contracted API package defines authentication, user and account operations, field geometry, imagery entitlements, index products, meteorological-station data, alerts, reporting, versioning, and OpenAPI specifications. The endpoint and entitlement matrix is attached to the project scope so every production integration uses a controlled API version.

Production integration is designed from the contracted endpoint matrix and OpenAPI specification supplied with the selected data products.

For satellite procurement and validation, ask:

1. Which current products and commercial tiers use Sentinel-2, a commercial high-resolution imagery source, or another imagery source?
2. Is a quoted 10 m or 3 m value native ground sample distance, processed pixel size, resampled output resolution, or display resolution?
3. How are cloud, cirrus, cloud shadow, haze, snow, smoke, and missing pixels detected and masked?
4. Is the displayed layer based on one acquisition, a mosaic, a temporal composite, an interpolated surface, or a model-derived output?
5. What maximum cloud threshold is applied at scene level and at field level?
6. Can a user reject an image that is technically under a scene-level cloud threshold but unusable over the actual field?
7. How are field polygons clipped, buffered, simplified, or rasterized at parcel edges?
8. How are mixed pixels and adjacent-field leakage handled in small or narrow parcels?
9. Are indices calculated from surface reflectance, top-of-atmosphere reflectance, or another processed product?
10. Can the customer retrieve acquisition date, acquisition time, satellite/sensor name, scene identifier, cloud score, processing version, and source metadata through the dashboard, export, or API?
11. What happens when no acceptable image exists inside the requested date window?
12. Can a customer retrieve the raster or only a rendered PNG and parcel statistics?
13. Are source imagery and derived index outputs licensed for storage, internal redistribution, model training, or use in customer-facing products?
14. How are cross-sensor time series normalized if more than one satellite source is used?

What a robust vegetation-index workflow should look like

A useful crop-monitoring workflow is not simply “open NDVI and look for red areas.” A defensible operational process usually includes:

Field geometry → image eligibility → processing metadata → index or derived layer → within-field comparison → anomaly ranking → scouting → observation → action → outcome.

For each step, a buyer should decide what must be recorded.

Field geometry

The platform should preserve the polygon version used for an analysis. If a field boundary changes during the season, historical values may no longer be directly comparable unless the old geometry is retained.

Image eligibility

The user should be able to see why an image is included or excluded. “Cloud coverage 10%” at scene level is not enough when the 10% cloud happens to cover the entire field.

Spatial comparison

Compare both the parcel-level statistic and the spatial distribution. A 0.62 field average can represent a uniform field or a field split between strong and weak zones.

Temporal comparison

A change between two dates can result from crop growth, harvest, irrigation, cloud contamination, different solar conditions, sensor differences, or processing changes. Time-series analytics should preserve provenance.

Field verification

The software should help the agronomy team reach the right location. Useful operational fields include map coordinates, navigation link, anomaly severity, first-seen date, last-seen date, and a way to attach a scouting result.

Outcome recording

A platform becomes more valuable when the organization can connect the signal to the field outcome: confirmed nutrient issue, irrigation fault, disease symptom, weed pressure, lodging, harvest, no issue found, or another classified result.

For large portfolios, AgriTech.tr recommends asking whether anomaly ranking can be tuned by crop, growth stage, field size, commercial importance, contract status, and persistence across multiple images.

Weather, climate, severe-weather, and agronomic risk

Forecast horizons can be configured for 7, 10, or 15 days, with field-specific risk notifications, disease-probability alerts, severe-weather messages, and optional storm and hail tracking.

Extended data modules can cover weather, drought, long-term risk, accumulations, spraying suitability, frost, chilling, lightning, storm polygons, radar-derived events, alert delivery, and reporting workflows.

Relevant technical data products include:

• Drought context: PDSI, SPEI, and SPI values in a drought service.
• Long-term risk: a documented risk service returning results on a 12 × 12 km grid for the coordinate supplied.
• Accumulations: GDD, DSV, precipitation, convective precipitation, precipitation hours, evapotranspiration, potential evapotranspiration, and FAO reference evapotranspiration fields.
• Spraying suitability: suitable, unsuitable, and absolute-unsuitable hours in a documented v2 spraying response.
• Alert delivery: SMS, email, and JSON POST options in the alert-notification service.
• Nowcast-style events: lightning, storm polygons, and approaching precipitation context in the documented alert system.
• Alert configuration: location, radius, sleeping time for repeated lightning alerts, optional all-clear messages, custom weather conditions, and a customer-side user identifier in POST workflows.
• API-key limits: API-key authentication supplies apikey in a request header and that keys may have daily, monthly, or yearly limits and may not enable every API product.

The project endpoint catalogue identifies the supported response schema, satellite entitlement, notification channel, quota, retention period, and service level for every selected data product.

Weather data is not automatically field weather

For agronomic decisions, a buyer should distinguish at least four layers:

1. Forecast model output for a grid cell.
2. Radar or severe-weather detection for a geographic area.
3. Official or third-party station observations near the field.
4. On-field microclimate sensors at the crop or canopy environment.

These data can all be useful, but they are not interchangeable.

For frost, spraying, disease-risk, irrigation, and insurance workflows, ask:

• What source is used for each displayed weather variable?
• Is the value observed, forecast, interpolated, assimilated, or model-derived?
• What is the horizontal grid size?
• How is elevation handled?
• How often is the forecast reissued?
• Are forecast runs and issue times preserved?
• Is precipitation a point value, grid average, radar estimate, or accumulation?
• How are missing station records handled?
• Can the platform link a field to an on-farm sensor?
• If multiple sources disagree, which source drives the alert?
• Can a customer retrieve the source and model version that triggered a historical alert?
• Is a risk threshold calibrated by crop stage or is it a generic weather threshold?

A 2 km, 5 km, or 12 km gridded output may still be useful for portfolio-level screening. It should not automatically be treated as equivalent to an instrument installed in the target field.

Spraying-time guidance: a high-value feature that needs explicit rules

Spraying-suitability analysis combines humidity, precipitation, wind speed, temperature, leaf-wetness or disease context, forecast uncertainty, chemical label constraints, and local operating rules. The project defines suitable, unsuitable, and prohibited windows together with update frequency, alert lead time, station or grid source, and human approval.

For a farm or contract-production organization, this feature should be evaluated against the actual pesticide application workflow. Ask for:

• Weather variables and thresholds used.
• Forecast horizon and update frequency.
• Wind-speed reference height and unit.
• Gust versus sustained-wind treatment.
• Rainfall probability versus forecast rainfall amount.
• Humidity and temperature limits.
• Crop, product, nozzle, droplet-size, and label-specific customization.
• Temperature-inversion or drift-risk logic, where relevant.
• Local legal and label constraints.
• Whether the system produces an explanation for an unsuitable hour.
• Whether the user can override or annotate a recommendation.
• Whether the recommendation and underlying forecast are archived.

A generic “suitable for spraying” message should not replace the pesticide label, local regulation, agronomist judgment, or the operator’s assessment of actual field conditions.

Disease probability and AI advisory: use alerts to prioritize scouting

Customized notifications can combine disease-probability signals with AI-assisted agricultural guidance and field-scouting workflows.

For technical evaluation, a disease-risk alert should be treated as a decision-support signal and scouting trigger, not as a laboratory diagnosis. Ask for:

• Supported crop, cultivar, disease, and geography combinations.
• Required planting date, phenological stage, irrigation, and management inputs.
• Weather, leaf-wetness, humidity, rainfall, temperature, or other assumptions.
• Whether the model uses gridded weather, field sensors, satellite indices, user observations, plant-growth models, or combinations of these inputs.
• Model type and current version.
• Training and validation period.
• Alert threshold and probability-calibration method.
• Validation by crop, province, season, and disease.
• Sensitivity/recall, specificity, precision, false-positive rate, false-negative rate, or another appropriate metric.
• Missing-data behavior.
• Method for model drift or seasonal recalibration.
• Whether an agronomist reviews, approves, or can override the output.
• Whether the user can record scouting evidence and close an alert with an outcome.
• Whether the system separates “risk conditions are present” from “disease is likely present.”

For cooperatives and advisory teams, the most useful workflow may be:

alert → portfolio ranking → field assignment → scout visit → observation → agronomic decision → action → outcome record

The platform should then be evaluated by time-to-detection, scouting hours, verified-alert rate, missed-event review, field response time, and completeness of the decision record rather than by the number of alerts generated.

Generative AI and messaging channel advisory questions

Agronomic decision support can combine spraying-window analysis, a structured advisory workflow, conversational assistance, and personalized field recommendations. The contracted scope defines whether each output is rule-based, statistical, machine-learning based, retrieval-assisted, generative, or agronomist-authored; it also defines supported crops and languages, explanation, confidence, source traceability, model version, contraindications, escalation, and audit history.

Ask:

1. Is the response generated from a large language model, retrieval system, rules engine, agronomic knowledge base, or a combination?
2. Which agricultural sources are used?
3. Are source citations shown to the user?
4. Can the assistant access the user’s field, crop, satellite, weather, or alert history?
5. What personal or farm data are sent to third-party model manufacturers?
6. Is customer data used for model training?
7. Are conversations retained, and for how long?
8. What languages and Turkish agricultural terminology are supported?
9. How are pesticide, fertilizer, irrigation, and disease-treatment questions constrained?
10. Is there an agronomist escalation path?
11. Can the organization export advisory conversations for audit?
12. Is the generated answer tied to a model and prompt version?
13. How are hallucinations, unsupported product recommendations, and unsafe agronomic advice handled?

AgriTech.tr recommends that enterprise buyers test the assistant with known agronomic scenarios, ambiguous questions, contradictory field data, unsupported crops, and safety-sensitive pesticide questions before rollout.

B2B multi-parcel monitoring and enterprise use

The B2B configuration provides a single agricultural-management layer for monitoring thousands of parcels, comparing fields and orchards with agricultural, climate, satellite, and sensor data, generating reports, and retaining historical archives.

Enterprise use cases include:

• Contract agricultural production: remote and large-scale plant-health and climate monitoring across supplier fields.
• Banking and agricultural finance: parcel-, province-, and district-level forecasts plus sensor and satellite data for risk management, credit decisions, sustainability-focused investment, climate-risk detection, and ESG scoring.
• Insurance: historical climate data and satellite tracking for storm, flood, and drought risk context and early warning.
• Energy: climate data for production planning, resource allocation, renewable-energy context, and weather-driven demand analysis.
• Carbon farming: documented work around Measurement, Reporting, and Verification (MRV) for carbon-farming workflows.

The project separates SaaS, data, and API delivery because each model has different operational and commercial requirements. A dashboard subscription, a dataset licence, and an API integration have different requirements for ownership, data portability, auditability, support, and continuity.

Enterprise architecture checklist

Enterprise requirement	Questions to put into the RFP or pilot
Field onboarding	GeoJSON, KML, SHP, WKT, cadastral reference, CSV coordinates, API creation, bulk upload limits, coordinate systems, polygon validation, duplicate handling, geometry history, and error reports.
Organization model	Parent/child accounts, dealer/customer hierarchy, cooperative membership, contract grower, region, crop, advisor, field ownership, reassignment, and offboarding.
Identity and security	JWT or API-key model, SSO/SAML/OIDC, MFA, password policy, IP restrictions, service accounts, secret rotation, role model, admin privileges, audit log, and tenant separation.
API operations	Current base URL, OpenAPI spec, pagination, rate limits, quotas, idempotency, asynchronous jobs, retries, backoff, error taxonomy, sandbox, SDKs, versioning, and deprecation notice.
Alerts and events	App, SMS, email, messaging channel, webhook or POST support; signatures/authentication; retry policy; delivery logs; deduplication; acknowledgement; escalation; sleeping windows; and all-clear events.
Data portability	Raw data access, derived-data export, field history, image metadata, index values, alert history, advisory history, map rasters, vector zones, CSV/JSON/XLSX, and bulk account-closure export.
Data governance	Controller/processor roles, field-boundary ownership, source-data rights, derived-data rights, retention, deletion, backups, subprocessors, hosting region, cross-border transfer, and training use.
Service management	SLA, uptime calculation, support hours, severity definitions, response and resolution targets, maintenance windows, incident communication, disaster recovery, RPO, and RTO.
Model governance	Model identifier, input provenance, validation report, calibration, change notification, confidence/severity scale, human review, explainability, rollback, and archived historical outputs.
Commercial continuity	Plan changes, premium imagery, SMS or message cost, API overage, station or sensor cost, custom integration, data exit, contract termination, and transition support.

API architecture, integration, and entitlement matrix

Enterprise integrations are assembled from controlled API modules rather than an undocumented collection of endpoints. The project can include:

• Authentication, token refresh, secret rotation, login, logout, user activation, role management, and service accounts.
• Organization, subscription, field, parcel, and account administration.
• GeoJSON, KML, SHP, WKT, CSV-coordinate, and bulk polygon onboarding with geometry validation.
• Satellite acquisition metadata, cloud masks, imagery orders, vegetation-index rasters, parcel statistics, and time-series exports.
• Weather forecasts, station observations, rainfall, evapotranspiration, drought indices such as PDSI, SPEI, and SPI, and agronomic accumulations such as GDD and DSV.
• Spraying suitability, frost, chilling, lightning, storm polygons, severe-weather alerts, SMS, e-mail, webhook, and JSON POST delivery.
• Crop classification, soil-analysis, reporting, and enterprise data-delivery services where included in the project.

The entitlement matrix records the base URL, API version, OpenAPI document, enabled products, satellite sources, native and delivered resolution, quotas, rate limits, synchronous or asynchronous behavior, webhook security, retention, SLA, deprecation policy, and commercial overage rules.

API pilot tests recommended by AgriTech.tr

1. Authenticate, refresh, rotate, and revoke credentials.
2. Create or import 100–1,000 realistic field polygons, including invalid and edge-case geometries.
3. Request satellite data across clear and cloudy date windows and reconcile dashboard values with the API.
4. Retrieve acquisition date, sensor, cloud score, processing version, index statistics, and source metadata.
5. Exercise premium-imagery ordering, asynchronous status, retry, timeout, and entitlement errors.
6. Query weather or station data at known coordinates and compare grid values with field observations.
7. Trigger test alerts and verify webhook authentication, retries, duplicate handling, ordering, acknowledgement, and audit logs.
8. Exhaust a test quota and record error codes, headers, backoff behavior, and commercial overage handling.
9. Export a complete field history, then test field deletion, user removal, account export, and access revocation.
10. Re-run a saved integration test after an API, processing, or model update and record technical-support response time.

Agricultural finance, insurance, and ESG use: validation requirements are higher

Satellite and climate intelligence can support banking, insurance, ESG, carbon, sourcing, and portfolio-risk workflows. These uses require stronger source lineage, reproducibility, model governance, human review, challenge procedures, and audit exports than a farmer-facing scouting map.

For credit, insurance, or ESG use, ask:

• What decision is the output allowed to influence?
• Is the output advisory, ranking, eligibility, pricing, trigger, or automated decision input?
• What is the geospatial unit: pixel, parcel, district, province, or 12 × 12 km risk grid?
• What is the source date and historical period?
• Can the result be reproduced later using the same input and model version?
• Is the source data legally licensed for this decision purpose?
• How is a field boundary linked to the applicant, policy, or supplier record?
• How are disputed boundaries or crop types handled?
• How are missing satellite observations treated?
• What is the process for human review?
• Can an affected farmer or customer challenge an output?
• What bias, calibration, and geographic validation work has been performed?
• What happens after a model update?
• Is there a complete audit trail?

A parcel-level risk score is not automatically credit-decision-grade, insurance-grade, or regulatory-grade merely because it is generated from satellite and climate data.

Carbon farming and MRV evaluation

Carbon-farming projects can use the platform as a data and workflow layer for Measurement, Reporting, and Verification (MRV). The project separates direct measurements, farm-activity records, remote-sensing observations, modelled outputs, uncertainty, audit evidence, methodology alignment, permanence or reversal controls, and independent verification responsibilities.

For an MRV project, buyers should separate:

• Measured data: direct field or laboratory observations.
• Activity data: planting, tillage, fertilizer, residue, irrigation, and land-management records.
• Remote-sensing observations: satellite-derived land cover, vegetation, residue, or change indicators.
• Modelled outputs: estimated soil carbon or emissions based on models.
• Verification evidence: records and controls made available to an auditor or programme.

Ask which methodology or carbon programme the workflow supports, which variables are measured versus modelled, how uncertainty is calculated, how reversals and permanence are handled where relevant, how field boundaries and historical land use are documented, and what export is produced for an independent verifier.

The presence of NDVI, NDTI, residue index, climate data, or field histories does not by itself establish compliance with a specific carbon standard.

Best-fit users and workflows

Farmers and farm managers

Best suited to users who want a combined view of field boundaries, satellite crop-health context, weather, spraying windows, and alerts. The key test is whether the platform reduces the time between seeing a risk signal and inspecting the correct part of the field.

Agronomists, cooperatives, and advisory teams

Useful where one team supervises many farms and needs a ranked scouting queue. Compare multi-user permissions, portfolio filters, alert triage, field assignments, field notes, history, image metadata, and the ability to move from a regional overview to parcel-level evidence.

Contract-production and food-chain companies

B2B monitoring is suitable for large parcel portfolios and contract-production operations. Buyers should test supplier and field onboarding, regional comparison, exception reporting, crop-stage context, data export, and integration with procurement, ERP, or supplier master data.

Insurers, banks, and agricultural finance teams

Insurance and finance configurations can combine climate history, forecasts, satellite observations, and field-level risk context. These organizations should distinguish decision-support features from audit-grade or automated-decision evidence and independently validate model governance, lineage, geospatial precision, reproducibility, and challenge processes.

Developers and enterprise data teams

Relevant where an organization wants agricultural or climate data inside its own application, ERP, ESG report, underwriting process, advisory tool, or risk engine. API versioning, entitlements, data rights, quotas, asynchronous jobs, webhooks, bulk delivery, and service terms are more important than the farmer-app feature list.

Climate-risk and parametric-insurance teams

Parametric-insurance projects can use the platform as a governed data and trigger-analysis layer. These teams should focus on trigger source, index definition, basis risk, observation hierarchy, data outage rules, trigger reproducibility, audit trail, payout responsibility, and the contractual boundary between data service and insurer.

Carbon and ESG teams

Relevant for organizations evaluating remote-sensing and climate-data inputs for MRV or sustainability workflows. The methodology, uncertainty treatment, data lineage, measurement-versus-model boundary, programme alignment, and independent verification path should be checked separately.

Subscription, API, and service configuration

The commercial package is sized by monitored area, number of fields and users, imagery source, forecast horizon, API volume, notification traffic, data retention, integration work, and support level—not by a predefined package name.

Capability group	Configurable project scope	Commercial definition
Core field monitoring	Field boundaries, NDVI, meteorological data, map history, and 7-day weather context.	Fields, hectares, users, revisit cadence, archive depth, and report frequency.
Expanded crop intelligence	NDRE, SAVI, NDMI, NDWI, terrain layers, 10-day forecasts, alerts, scouting queues, and agronomic workflows.	Supported crops, analytics, notification channels, training, and validation scope.
Advanced risk package	15-day forecasts, storm and hail tracking, drought, frost, spraying suitability, premium imagery, and sensor or station integration.	Data entitlements, station hardware, message volume, premium-image orders, and support SLA.
Enterprise data and API	Multi-organization B2B dashboard, API library, bulk onboarding, data delivery, SSO, audit logs, custom integration, and governed export.	API calls, quotas, service accounts, data rights, custom work, uptime, response targets, and exit assistance.

The quotation also defines premium imagery, SMS or messaging costs, data-export rights, custom integrations, onboarding, training, support hours, SLA, backups, and retention after contract completion.

Field validation protocol before a large rollout

A practical evaluation should use real fields and a pre-agreed scoring method.

Phase 1 — Data setup

• Import representative small, medium, and large parcels.
• Include irregular boundaries, narrow fields, adjacent fields, sloped land, and fields with known management zones.
• Record crop, planting date, irrigation status, and known field issues.
• Preserve the source and version of every test boundary.
• Measure onboarding time and geometry-cleanup effort.

Phase 2 — Satellite review

• Compare displayed image dates with independent acquisition metadata where possible.
• Check cloud and cloud-shadow handling.
• Test fields that are cloud-covered while the wider scene is mostly clear.
• Walk high and low index zones with georeferenced observations.
• Compare parcel averages with within-field variability.
• Repeat across crop stages.
• Include at least one low-cover and one dense-canopy period.
• Test a small field where edge pixels matter.
• Record false anomaly signals.
• Compare the same field across sensors if multiple imagery sources are available.

Phase 3 — Weather and alert review

• Compare forecast and historical outputs with nearby trusted station observations where available.
• If a field sensor is used, compare sensor and gridded values.
• Record alert issue time, event time, field location, threshold, channel, and operator response.
• Review missed events as well as successful alerts.
• Test disease or agronomic alerts with an agronomist and documented scouting findings.
• Record duplicate alerts and alert fatigue.
• Check whether alert history can be exported.

Phase 4 — AI and advisory review

• Test known agronomic scenarios.
• Test unsupported crops and ambiguous inputs.
• Ask the same question in different wording.
• Test whether the assistant cites sources or explains uncertainty.
• Test safety-sensitive pesticide and fertilizer questions.
• Record hallucinations or unsupported certainty.
• Confirm escalation to a qualified human where promised.

Phase 5 — Enterprise and API review

• Reconcile dashboard and API values.
• Test batch operations, quotas, errors, retries, and asynchronous jobs.
• Export the full pilot history.
• Test user removal and field reassignment.
• Verify every contracted endpoint, schema, entitlement, and service level in the acceptance environment.
• Measure support response during an actual technical issue.

Phase 6 — Outcome review

Do not use a generic claim such as “AI increased yield” as the acceptance metric. Define operational measures such as:

• Scouting hours per 1,000 hectares.
• Time from risk detection to inspection.
• Percentage of alerts verified in the field.
• False-positive rate in the pilot.
• Known events missed by the platform.
• Reduction in unnecessary field visits.
• Time required to onboard 1,000 parcels.
• Percentage of fields with boundary errors.
• API integration effort.
• Alert-delivery reliability.
• Data-export completeness.
• Decision-record completeness.
• Total annual cost including premium imagery, messaging, hardware, onboarding, and custom development.

Performance validation and project acceptance

A satellite vegetation index is not by itself a crop-disease diagnosis. A weather grid is not automatically equivalent to an on-field weather station. Disease probability, spraying suitability, AI advice, parcel risk scores, carbon indicators, and insurance triggers become operationally useful only when their source, model version, resolution, uncertainty, and field-validation procedure are defined.

The project acceptance plan therefore includes:

• Ground-truth scouting for selected crops, regions, seasons, and growth stages.
• Confusion matrices or error measures for classification, alerts, and risk bands.
• Station-versus-grid comparison for temperature, rainfall, humidity, wind, and severe-weather events.
• Acquisition, cloud-mask, processing-version, and source lineage for every satellite layer used in a decision.
• Human review and escalation for disease, pesticide, fertilizer, irrigation, finance, insurance, ESG, and regulatory workflows.
• Reproducibility tests after imagery, API, model, or processing updates.
• Data-governance, audit-trail, challenge, rollback, and export procedures.

Acceptance criteria are tied to the actual operational decision: scouting priority, irrigation review, spraying window, contract-production monitoring, portfolio reporting, risk analysis, or API integration.

AgriTech.tr project scope for satellite crop intelligence and agricultural APIs

The sourcing brief can be used to define the application, technical interfaces, documentation, and service requirements before supplier research begins.

For project configuration, technical quotation, RFP criteria, pilot design, API evaluation, field-validation protocols, or identifying alternative Turkish AgriTech solutions, contact info@agritech.tr.

Image-use note

The cover and crop-scouting photographs provide field-monitoring context and are used under the Pexels License. Photographer and source credits are retained in the reference section.

Procurement and project delivery

AgriTech.tr can structure the technical requirement and compare current supplier responses for the buyer’s project. Installation, commissioning, operator training, warranty, spare-parts, and after-sales scope must be confirmed in each supplier quotation. The final scope should be documented against the approved application, capacity, site conditions, destination, and delivery schedule in the selected supplier quotation and contract.