Ag-GIS Data Fundamentals & Spatial Reference Systems
Coordinate reference systems, vector and raster ingestion, projection discipline, and the architectural patterns that keep production pipelines spatially honest.
Python · GIS · Drone · ISOBUS
Precision Ag Python & GIS Automation
Architectural patterns and Python implementations for production-grade precision agriculture pipelines — built for agtech engineers, farm data analysts, and GIS developers who treat spatial accuracy as a first-class engineering concern.
Reliable yield maps and variable-rate prescriptions start with disciplined coordinate reference systems, radiometrically calibrated imagery, and deterministic batch pipelines that scale from single-farm pilots to regional fleets without losing topology, metadata, or compliance trails.
Three end-to-end production playbooks below — with working
rasterio, geopandas, pyproj,
and ISOXML examples engineered for repeatability, equipment compatibility,
and audit-ready reporting.
Data Fundamentals
Coordinate reference systems, vector and raster ingestion, projection discipline, and the architectural patterns that keep production pipelines spatially honest.
Explore the pillarImagery & Indices
End-to-end UAV pipelines: radiometric correction, masking, band math, NDVI / NDRE / SAVI, temporal aggregation, and threshold-driven prescription export.
Explore the pillarYield & VRA
Combine telemetry, spatial interpolation, management-zone classification, ISOXML / shapefile export, and ISOBUS-ready variable-rate maps.
Explore the pillarStart here
Data Fundamentals
Understanding CRS in Precision Agriculture
Why coordinate reference systems matter on every farm, how WGS 84 vs. UTM trade-offs affect measurement accuracy, and the patterns that keep pipelines projection-honest.
Data FundamentalsHow to Convert WGS84 to UTM for Farm Mapping
Step-by-step pyproj and geopandas workflow for projecting field boundaries and yield-monitor points into a metre-accurate UTM zone.
Calculating NDVI and NDRE with Rasterio Step by Step
Band-math fundamentals for multispectral drone tiles: reading reflectance arrays, computing NDVI and NDRE, masking invalid pixels, and writing GeoTIFFs with correct metadata.
Imagery & IndicesPython Script for SAVI Calculation on Drone Tiles
Soil-adjusted vegetation index implementation for low-canopy-cover fields — tuning the L correction factor and batching across multi-tile drone surveys.
Yield & VRAInterpolating Sparse Yield Monitor Data with Kriging in Python
Variogram fitting, ordinary kriging, and cross-validation with scikit-gstat — turning irregular combine telemetry into smooth, field-resolution yield maps.
Exporting Prescription Maps to John Deere GreenStar Format
ISOXML task-file structure, rate-layer encoding, and shapefile packaging for GreenStar 3 / Operations Center — with Python validation against the ISO 11783-10 schema.
Three production-grade pillars
The site is organized around three end-to-end playbooks. Each pillar covers the schemas, Python patterns, and operational constraints that turn raw field data into ISOBUS-ready prescriptions.
Drone Imagery Processing & Vegetation Index Workflows
End-to-end UAV pipelines: radiometric correction, masking, band math, NDVI / NDRE / SAVI, temporal aggregation, and threshold-driven prescription export.
Yield Mapping & Variable Rate Prescription Generation
Combine telemetry, spatial interpolation, management-zone classification, ISOXML / shapefile export, and ISOBUS-ready variable-rate maps.
Engineered for the realities of precision agriculture
Every guide is grounded in operational constraints: GPS drift, RTK precision budgets, FMIS interoperability, regulatory input-rate caps, and the unforgiving feedback loop between prescription accuracy and acre-level ROI. Code blocks are runnable, syntactically complete, and ready to drop into Python batch workers, Dask clusters, or ISOBUS export pipelines.