GIS

Crop Health Monitoring, Change Detection & Yield Estimation

A multi-season geospatial programme that flags crop stress early, tracks how cropped area changes year on year, and estimates yield before harvest.

Client: Agricultural Programme OperatorAgricultureProject lead: M. A Zia

The challenge

The client was managing crops across a large area with three blind spots: stress was noticed only once it was visible on the ground and already costly, nobody could say reliably how cropped area had shifted between seasons, and yield was a guess until the harvest was actually in. Procurement, storage and credit decisions were all being made on that guess.

Our solution

We put a multi-temporal satellite stack behind all three questions. Health indices computed per field surface stress zones early enough to act on. Supervised classification of each season, compared post-classification, shows exactly where crop area expanded, contracted or switched type. Yield is modelled by regressing peak-season index values against field-sampled harvest records, so the estimate is calibrated to the client's own ground data rather than a generic curve. Everything is delivered as maps and season reports the operations team reads without needing GIS training.

Results & impact

  • Crop stress surfaced from imagery before it was visible on the ground
  • Season-to-season cropped-area change quantified per zone, not estimated
  • Pre-harvest yield estimates calibrated against the client's own field samples
  • Procurement and storage planning based on mapped evidence

Technology

Sentinel-2LandsatArcGISERDAS ImaginePythonSupervised Classification

Skills applied

Remote SensingImage ClassificationChange DetectionYield ModellingSpatial StatisticsQualitative Research

Let's build something that delivers

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