portal.groundx.ag

Canopy Growth

Autor: Admin

This algorithm evaluates canopy growth dynamics after agronomic treatment applications. It is designed to support product performance validation by quantifying canopy expansion trends before and after treatment at parcel level. Input Data The model processes georeferenced agronomic context: Parcel geometry (polygon or parcel ID) Product identifier and application metadata Application date and optional dosage Multi-date satellite observations Statistical Method The workflow builds pre/post comparison windows, applies temporal smoothing, and estimates treatment effect using baseline-adjusted growth trajectories. Output Metrics The service returns structured statistical outputs: Weekly canopy growth delta Relative uplift score vs baseline Confidence band by observation density Data quality flags and anomaly notes

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Delayed Senescence

Autor: Admin

agriculture senescence crop-health

This algorithm estimates treatment-driven senescence delay by comparing post-application decline patterns against historical reference behavior. It is intended for efficacy assessment and campaign reporting. Input Data The analysis requires: Parcel identifier or geometry Product and application date Optional management events (stress, irrigation) Time-series spectral maturity indicators Statistical Method The model detects inflection points, fits decline curves, and computes delay-in-days versus control baselines with uncertainty estimation. Output Metrics The service returns standardized indicators: Estimated senescence delay (days) Confidence interval by data completeness Baseline vs treated trajectory score Trend stability diagnostics

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Greenness Gain

Autor: Admin

agriculture ndvi greenness

This algorithm quantifies treatment-attributable greenness gain while controlling for seasonal effects. It supports benchmarking, agronomic reporting, and product comparison analyses. Input Data The algorithm uses: Parcel boundaries and crop-cycle context Product and treatment date Vegetation indices (NDVI, EVI) Optional control parcels or historical baseline Statistical Method The pipeline applies temporal normalization, quality filtering, and delta decomposition to isolate treatment effect from background variability. Output Metrics The service returns: Absolute and relative greenness gain NDVI/EVI contribution breakdown Intra-parcel variability statistics Effect-size summary for reporting

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Canopy Cooling

Autor: Admin

agriculture thermal canopy

This algorithm estimates canopy cooling effect after treatment by analyzing thermal time-series response. It supports stress-mitigation evaluation and water-efficiency monitoring. Input Data Required inputs: Parcel geometry Product and application date Thermal and optical observations Optional weather normalization context Statistical Method The engine computes thermal anomalies, aligns treatment windows, and estimates adjusted cooling effect against expected baseline behavior. Output Metrics The service provides: Cooling effect score Thermal anomaly trend Treatment vs baseline differential Confidence and quality diagnostics

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Crop Vigor

Autor: Admin

agriculture vigor pdf-report

This algorithm generates a portfolio-level PDF report describing crop vigor evolution across multiple parcels and a selected date range. Input Data The report engine processes: Parcel list Start and end dates Vigor indicators from satellite signals Optional grouping metadata (farm, variety, campaign) Statistical Method The report aggregates parcel-level trajectories, computes percentile bands, identifies outliers, and summarizes trend consistency over time. Output The generated PDF contains: Executive summary and methodology Weekly vigor distribution charts Aggregated statistics and trend tables Outlier detection and interpretation notes Recommendation section for technical review

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Crop Chlorophyll

Autor: Admin

agriculture chlorophyll pdf-report

This algorithm generates a PDF report focused on chlorophyll dynamics across multiple parcels in a selected time window. Input Data The workflow consumes: Parcel collection Date interval for analysis Chlorophyll-related indices Optional agronomic segmentation metadata Statistical Method The pipeline evaluates chlorophyll trend stability, cross-parcel variability, and spatial heterogeneity with aggregated summary indicators. Output The final PDF includes: Chlorophyll trend charts Cluster comparison tables Statistical summaries and anomaly flags Interpretation notes for field decision-making

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Crop Moisture

Autor: Admin

agriculture moisture water-stress

This algorithm generates a PDF report with aggregated crop moisture evolution for a parcel portfolio and selected date range. Input Data The report engine uses: Parcel list Date range Moisture-related indices Optional weather normalization context Statistical Method The workflow aggregates parcel trajectories, measures trend stability, and detects likely water-stress periods using threshold-based scoring. Output The resulting PDF includes: Moisture evolution timelines Group-level statistics Water-stress alerts and severity classes Irrigation-oriented recommendation notes

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Prescriptions

Autor: Admin

agriculture prescriptions decision-support

This algorithm generates agronomic prescriptions for a parcel within a selected period, transforming observed crop patterns into prioritized field actions. Input Data The prescription engine requires: Parcel geometry or ID Date range Remote sensing indicators and trend context Optional intervention history Decision Logic The model detects limiting factors, scores action impact, and prioritizes recommendations using rule-based and statistical weighting. Output Returned recommendations are structured and auditable: Nutrition adjustment proposals Irrigation strategy suggestions Operational timing windows Rationale and confidence score per action

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VRA Map

Autor: Admin

agriculture vra precision-farming

This algorithm builds a variable-rate application map for a selected parcel, optimized for precision farming execution. Input Data The mapping process considers: Parcel boundary Agronomic target variable Spatial variability layers from satellite analysis Optional machinery constraints Statistical Method The workflow performs zoning based on spatial clustering, classifies dose recommendations, and validates continuity for field operations. Output The service returns machine-ready map deliverables: Zone polygons with target rates Class distribution statistics Exportable geospatial layers Quality checks and processing assumptions

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Historical Satellite

Autor: Admin

agriculture satellite-history ndvi

This algorithm retrieves historical satellite time series for a parcel, date range, and selected index (e.g., NDVI, ETc, EVI). Input Data Users provide: Parcel ID or geometry Date range Target index Optional temporal granularity Data Processing The pipeline filters low-quality captures, harmonizes temporal spacing, and computes quality-aware summary statistics. Output The service returns structured historical analytics: Timestamped index values Optional smoothed series Data quality flags Descriptive statistics and trend summaries

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