This is the Praxium Labs view from real engagements with Nepali businesses on the ground. Crop yield prediction is the friendliest "first ML project" for AgriTech in Nepal — well-bounded inputs, public training data, and a clear consumer (cooperatives, microfinance, government planners).
Data sources
- Sentinel-2 satellite imagery: free, 10m resolution, NDVI/EVI bands. Available via Sentinel Hub or Google Earth Engine
- DHM weather data: daily temp + precipitation by station
- Cooperative yield records: 3–5 years per crop, by ward or VDC. Source: local cooperatives, MoALD records
- Soil data: SoilGrids global dataset (250m), supplemented by NARC soil surveys
- Crop calendar: planting / harvest dates by district, available from NARC and MoALD
- Elevation: SRTM 30m DEM, free
Features that matter
- Peak-growing-season NDVI
- Cumulative GDD (growing degree days)
- Total monsoon precipitation
- Heat-stress days (days > 35°C during flowering)
- Soil organic carbon and pH
- Elevation and slope
- Variety planted (categorical)
- Previous year's yield on same plot (lag feature)
Model choice
Gradient-boosted trees (LightGBM or XGBoost) win on tabular agronomic data. Random forests are competitive and slightly simpler. Deep learning rarely beats tree ensembles at typical Nepali data volumes (single-digit thousands of records per crop).
Validation
Time-aware cross-validation — train on years 1-3, validate on year 4, test on year 5. Random k-fold leaks information across time and gives misleadingly high accuracy. Report MAE per hectare and MAPE; benchmark against the naive baseline of "this year = last year".
Deployment
Cooperative-level: model outputs a forecast per ward/VDC per crop per season → cooperative manager reviews → drives procurement and storage decisions. Farmer-level: SMS / IVR with growing-season forecast for the farmer's area → adjust input application timing. On-device offline: TensorFlow Lite version of the model in a Nepali smartphone app for cooperative field agents.
What it costs and pays back
- Build: NPR 8–25 lakh (mostly data wrangling and validation, not modelling)
- Annual cost: NPR 1–3 lakh for satellite + hosting + retraining
- Value: primarily indirect — better procurement planning by cooperatives, reduced input over-application, basis for crop insurance products
Why most yield-prediction projects fail
- Insufficient ground-truth data: need 3+ growing seasons of plot-level yield data; most operators have less
- Wrong granularity: district-level predictions are too coarse for operational decisions; plot-level needs sensor / satellite data per plot
- Ignoring smallholder reality: models built for 100-hectare commercial farms do not transfer to 0.5-hectare smallholder context — see our AgriTech context
- No closed loop: predictions made; not delivered to farmer in actionable form; not measured against realised outcome
- Climate non-stationarity: historical patterns shifting; models that ignore climate trend produce overconfident predictions
The minimum viable model
For a Nepali cooperative starting from scratch: a simple regression on (variety, planting date, rainfall sum to date, NDVI 30-day average from Sentinel-2, days-since-last-spray) predicts realistic yield within ±15-20% by mid-season for most cereal crops. Build this baseline first; complex deep-learning models often do worse on small Nepali datasets. Add complexity only when you have validated the simple model and have enough new data to train against.
Frequently asked questions
Which crop should I start with?
Rice (paddy) is the most studied with the most public data. Maize is next. Wheat works but data is sparser. Cash crops (cardamom, tea, coffee) need bespoke data — much harder for a first project.
Can I get usable accuracy from satellite alone?
For relative ranking (which district is higher-yielding this year): yes. For absolute yield per hectare: ground-truth records help significantly. Hybrid (satellite + cooperative records) is the sweet spot.
What about climate-change shifts in the patterns?
Use a sliding-window training set (last 5–8 years) and retrain annually. Long historical data from the 1990s on a stationary climate-assumption no longer represents current conditions in Nepal.
Is this the right project for a non-profit / NGO?
Often yes — funders (World Bank, ADB, ICIMOD) actively fund agricultural ML pilots in Nepal. The build cost lands inside typical grant sizes.
Can a farmer use this directly?
Not without intermediation. The model output goes to a cooperative or NGO that translates it into action recommendations the farmer can act on. Direct farmer-app delivery requires both smartphone penetration and clear value-per-prediction — usually not the right MVP.
Where do I get satellite imagery?
Sentinel-2 (free, 10m resolution, weekly revisit) covers Nepal. Planet (commercial, daily, sub-meter) for premium use cases. Both have Python clients (sentinelhub, planet) that scale to many plots.
Can a Nepali farmer get a smartphone app for yield prediction?
Technically yes; practically the cooperative-led delivery model works better — see the agritech post above. Farmer-direct apps face smartphone-ownership and literacy barriers.
Who can build this in Nepal?
Praxium Labs — Nepal's AI and automation consultancy, based in Lalitpur — designs and builds the systems described in this guide for Nepali businesses and for international teams hiring from Nepal. Start a project or see all services.