Turn regenerative agriculture commitments into continuous, plot-level evidence. Monitor where practices are implemented, how they change over time, and where intervention is needed across thousands of farms.
Regenerative agriculture programs increasingly span thousands of farms, multiple suppliers, and different production systems. But understanding what is actually happening on the ground still relies heavily on farmer declarations, periodic surveys, and sampled field visits.
That creates a fundamental visibility gap. Teams may know which practices they want farmers to adopt, but not consistently where they are present, whether they are being maintained, or how they are changing over time.
Different practices also leave different signals. Tree cover and vegetated buffers are persistent features. Cover crops and tillage appear seasonally. Crop rotations only become visible through a multi-year history. No single snapshot can capture them all.
As a result, sustainability and sourcing teams are left asking:
Insights Hub turns satellite observations and geospatial data into consistent evidence of regenerative practices across the supply base — from persistent tree and landscape features to seasonal management practices and multi-year cropping patterns.
Monitor the structure, establishment and persistence of perennial vegetation:
Observe management practices changing within and between growing seasons.
Build a historical record of what happened on each plot — not just what is visible today.
Verify whether regenerative production protects the ecosystems around the farm.
Different practices require different evidence. Insights Hub combines satellite time series, GeoAI, land-cover and environmental datasets to interpret the signals relevant to each practice — from tree height and canopy structure to vegetation cover, exposed soil, waterways and multi-year crop history.
Validate supplier geolocation and plot boundaries before analysis to create a consistent spatial foundation for practice monitoring across suppliers, regions, and seasons.
Different practices leave different spatial and temporal signatures. Picterra applies the appropriate combination of canopy, vegetation, soil, land-cover, and contextual signals rather than forcing every practice through a single detection method.
Crop identification and historical crop context help interpret what satellite observations mean for a particular production system — creating a stronger foundation for practice verification, rotation analysis and change detection.
Combine current observations with satellite time series to establish baselines, identify when practices or transitions first appeared, and monitor whether they persist over time.
