Regression
Yield model
Predict tonnes per hectare from crop, planting date, soil, historical yield, weather, and input rate features.
Start with: Random Forest or Gradient Boosting; compare against linear regression.
Architecture for a realistic prototype
Use separate models for the things that actually drive profit.
Do not train one black-box “profit model” on loosely matched data. Build small, explainable components, then join their outputs in a transparent profit equation.
End-to-end decision flow
Farmer input → processing → models → profit estimate → recommendation → farmer decision.
- 1
1. Farmer input
A short, farmer-friendly field plan: crop, area, soil result, costs, local price, and visible risk.
- 2
2. Data processing
Validate units, aggregate weather by growing stage, calculate per-hectare costs, and flag missing data.
- 3
3. ML models
Yield regression + disease-risk classification + price forecasting, each trained and evaluated separately.
- 4
4. Profit estimation
Combine conservative yield and price estimates with direct variable costs, transport, and other known costs.
- 5
5. Recommendation
Rank a small set of feasible actions by expected margin and downside risk; show the reason, not only a score.
- 6
6. Farmer decision
Farmer reviews the assumptions, changes a cost or price, and chooses whether to plant, treat, irrigate, or sell.
What to model—and with which beginner-friendly algorithms
Classification
Disease / pest risk
Predict low, medium, or high risk from crop stage, weather, scouting data, and optionally a leaf image.
Start with: Logistic Regression or Random Forest; only use CNN images if you have time.
Time-series regression
Selling price
Forecast a realistic price range by commodity, market, season, and lead time to harvest.
Start with: Seasonal baseline, then Random Forest with lag features.
Recommendation + optimization
Plan selection
Compare only feasible crop or input plans, then rank by expected margin and farmer-set risk tolerance.
Start with: A rules-based ranking table before advanced optimization.
Profit equation
Expected revenue = expected marketable yield × expected selling price
Expected cost = seed + fertilizer + pesticide + weedicide + labor + fuel + irrigation + equipment + transport + storage + finance/insurance
Expected profit = expected revenue − expected cost
Expected cost = seed + fertilizer + pesticide + weedicide + labor + fuel + irrigation + equipment + transport + storage + finance/insurance
Expected profit = expected revenue − expected cost
For a first demo, estimate yield and price ranges independently, use farmer-entered costs, and compute a low / expected / high profit range. This is clearer and safer than pretending a single estimate is exact.
Hackathon scope that works
- • Start with one region and 1–2 crops.
- • Collect one tidy table per field-season, not dozens of unrelated files.
- • Use a transparent baseline before adding an ML model.
- • Compare two or three actionable plans, not every possible farm decision.
- • Show assumptions, confidence, and a human override.