GIS Agriculture Production Deployed

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 SECTOR

Agricultural Programme Operator

DELIVERY ARCHITECT

M. A Zia

DELIVERY STATUS

100% Complete & Active

IP & CODE OWNERSHIP

Full Client Handover

THE OPERATIONAL BOTTLENECK & 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.

THE CUSTOM ARCHITECTURE & 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.

Quantifiable Business Outcomes

Measurable Results & Return on Investment

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 Stack Applied

Sentinel-2 Landsat ArcGIS ERDAS Imagine Python Supervised Classification

Direct Engineering Consultation

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