Crop yield estimation with satellite imagery
سیٹلائٹ تصویر سے فصل کی پیداوار کا اندازہ
22 min read
Three ways to see it
What the models use. Sentinel-2 provides ten meter resolution imagery every five days. Landsat adds historical depth. NDVI, EVI, and other vegetation indices capture how green the field is over time. Adding rainfall, temperature, and historical district-level yields, a regression or neural model converts indices into estimated yield per hectare. The error band matters; report it alongside the central estimate.
Cadastral and ground truth challenges. To estimate yield per field you need to know which pixels belong to which field and what crop is growing there. In Pakistan field boundaries are not consistently digitized; many farmers do not know their exact area in hectares. Crop classification models that identify wheat versus mustard versus fodder from imagery alone improve as you collect ground truth labels each season.
Stakeholder engagement. The bureau of statistics, the provincial crop reporting service, and the Ministry of National Food Security want the methodology documented before they accept your numbers. Bring a technical note, a comparison against historical official estimates, and a clear plan for how the model will be updated. Skipping this step means your model gets called unofficial even when it is more accurate.
Quick check
Quick check: what makes modern AI different from a rule-based program?
The why-tree
Equity and small farmers. Models tend to perform better on large, uniform fields and worse on small, mixed cropping plots that dominate parts of Sindh and KP. Report performance separately for these. If your output is used for policy, an undercount in small holdings can route resources away from those who need them most. A safety floor that defaults to the district average if confidence is low protects the small farmer.
Try this with Claude
Pilot pathway. One: pick one crop and one province with cooperative ag department. Two: license a starting set of satellite imagery and pair it with district level historical yields. Three: train a baseline regression model and a stronger neural model and compare. Four: ground truth on fifty randomly chosen fields per district. Five: publish a public dashboard with error bands. Six: invite the bureau of statistics to validate.