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Problem Statement 6

"Krishi-Route" - Profit & Logistics Optimizer​

Focus: Geospatial Analysis | Market Intelligence | Route Optimization | Profit Maximization

1. Background:​

India has 7,000+ Agricultural Produce Market Committees (APMCs), yet 70% of farmers sell at the nearest mandi without checking prices elsewhere. A farmer selling onions 10km away might earn ₹20,000, while traveling 50km could yield ₹24,000 profit after transport costs.

2. Current Problem:​

1. Fear of Transport Costs

  • Farmers assume longer distance = lower profit
  • Don't calculate net profit (revenue - transport cost)
  • Impact: Leave ₹5,000-10,000 on the table per trip

2. No Price Comparison Tools

  • Agmarknet shows prices but not profitability
  • Can't see which mandi maximizes earnings
  • Impact: Sell at suboptimal markets

3. Logistics Blind Spot

  • Don't know truck rental rates per km
  • Miss pooling opportunities with neighboring farmers
  • Impact: Pay 40% more on transport than necessary

3. Goal:​

Build "Google Maps for Farmers" that shows most profitable routes, not just fastest routes.

Beyond navigation → Create a platform that:

  1. Fetches real-time market prices across mandis
  2. Calculates net profit (price - transport - handling)
  3. Recommends optimal selling location
  4. Visualizes routes and profit comparisons

4. Expected Solution (MVP Requirements):​

1. Input Module​

Capture trip details:

  • Crop Type: Dropdown (Onion, Wheat, Tomato, etc.)
  • Quantity: Input in tons/quintals
  • Vehicle: Select (Tata Ace, Tractor, Truck)
  • Location: Auto-detect or manual pin drop

Output: Structured trip query

2. Market Data Fetcher​

Analyze nearby markets:

  • Price Source: Agmarknet API or mock dataset
  • Coverage: 3-4 mandis within 100km radius
  • Distance Calc: Google Maps API / Mapbox for km distance

Output: List of {mandi, price, distance}

3. Net Profit Algorithm​

Calculate profitability:

  • Revenue = Market Price × Quantity
  • Transport Cost = Distance × Vehicle Rate/km
  • Other Costs = Loading/unloading charges
  • Net Profit = Revenue - Total Cost

Demo Goal: Show side-by-side comparison where farther mandi yields higher net profit

4. Decision Dashboard​

Real-time visualizations:

  • Profit Cards:
    • Mandi A (10km): ₹20,000 profit
    • Mandi B (50km): ₹24,000 profit ⭐ Winner
  • Route Map: Visual path on interactive map
  • Breakdown: Revenue, costs, profit margin displayed
  • Impact Metrics:
    • Markets compared
    • Best profit margin identified
    • Potential savings shown

5. "Level Up" Features:​

Advanced Features (Choose 2-3)

  • Ride Share: Pool 2 farmers with 1-ton each → Save 40% on truck
  • Price Volatility Alerts: Warn if mandi price dropped 3 days straight
  • Perishability Factor: Flag risk for tomatoes on 200km trips
  • Historical Trends: "Mandi B usually peaks on Wednesdays"
  • Fuel Price Integration: Adjust transport costs based on diesel rates

6. Tech Stack:​

Core

  • Backend: Node.js/Express
  • Database: MongoDB (market prices, historical data)

Geospatial

  • Maps: Google Maps API / Mapbox (distance, routing)
  • Visualization: Leaflet.js, Deck.gl

Frontend

  • Framework: React.js
  • Charts: Recharts (profit comparisons)

External APIs

  • Agmarknet (market prices)