GIS

Sugarcane Harvest Monitoring using Multispectral Imagery

Satellite-based harvest-readiness mapping that told a cane supply team which blocks to cut first, instead of relying on field-by-field guesswork.

Client: Sugarcane Supply & Grower OperationsAgriculture & Agri-processingJan 2022 – Apr 2022Project lead: M. A Zia

The challenge

The client came to us during crushing season with a scheduling problem. Cane was being harvested block by block on the basis of field visits and habit, which meant some blocks were cut before they matured and others stood too long. Both cost sugar recovery, and with thousands of acres spread across a wide catchment, no team could physically inspect enough of it in time to fix the order.

Our solution

We built a monitoring workflow on multispectral satellite imagery. Field boundaries were digitised into a parcel layer, then each new cloud-free acquisition was processed into vegetation and moisture indices and tracked as a time series per block. Comparing each block's curve against its own crop calendar let us classify maturity and flag the blocks approaching peak. The output was a plain harvest-readiness map, refreshed with every satellite pass, that the supply team could act on directly.

Results & impact

  • Harvest sequencing driven by observed crop condition, not manual scouting
  • Whole catchment covered every satellite pass, without extra field teams
  • Early and late cutting reduced, protecting sugar recovery
  • A repeatable workflow the team can re-run each season

Technology

Sentinel-2ERDAS ImagineArcGISPythonNDVI / NDMITime-Series Analysis

Skills applied

Remote SensingMultispectral Image ProcessingVegetation Index AnalysisCrop PhenologySpatial AnalysisQualitative Research

Let's build something that delivers

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