🚁 drone_precision_ag

Drone-Based Precision Fertilisation via Decentralised Oracle

Autonomous drone fleet — multispectral NDVI mapping — micro-dose fertilisation

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🌍 The Problem

Uniform fertilisation is destroying the planet:

• 50–70% of applied fertiliser is wasted — it runs off into rivers and groundwater
• Nitrate pollution causes algal blooms, dead zones, drinking water contamination
• Soil degradation from over-fertilisation reduces long-term yield
• Global cost: $200B/year in wasted fertiliser + $300B/year in environmental damage

The root cause: Farmers apply the same dose per hectare everywhere — but every square metre of a field has different needs.

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✅ The Solution

An autonomous drone fleet that:

1. Flies the field — multispectral camera captures NDVI, NDRE, NDWI indices at 5cm resolution
2. Builds a stress map — ML model identifies exactly which zones need fertiliser, water, or nothing
3. Applies micro-doses — variable-rate spray nozzles deliver the exact amount needed, per square metre
4. Reports to oracle — all spray events logged to a decentralised oracle (Chainlink-compatible)
5. Learns continuously — yield data fed back to improve next season's prescription map

Field scan (NDVI) → Stress map → Prescription map → Micro-dose spray → Oracle log
      ↑_______________________________________________________________|
                         Federated yield feedback loop

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🧮 Core Algorithms

NDVI Computation

NDVI = (NIR - RED) / (NIR + RED)

NDVI < 0.2  : bare soil / dead vegetation → skip
NDVI 0.2–0.4: stressed crop → high fertiliser dose
NDVI 0.4–0.6: moderate growth → medium dose
NDVI > 0.6  : healthy crop → minimal or zero dose

Variable-Rate Prescription

dose(x,y) = D_base × (1 - NDVI(x,y)) × stress_factor(x,y) × soil_coef(x,y)

Constraints:
  0 ≤ dose ≤ D_max          (agronomic limit)
  Σ dose(x,y) ≤ budget      (economic limit)
  dose = 0 where NDVI > θ   (healthy zone skip)

Swarm Coordination

Coverage optimisation: Boustrophedon decomposition
  → field divided into parallel strips
  → each drone assigned non-overlapping strip
  → handoff protocol when battery < 20%

Collision avoidance: ORCA (Optimal Reciprocal Collision Avoidance)
  → each drone maintains 3m separation sphere
  → velocity obstacles computed in real-time

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🏗️ Architecture

drone_precision_ag/
├── core/
│   ├── ndvi_engine.py          # Multispectral index computation (NDVI, NDRE, NDWI)
│   ├── prescription_map.py     # Variable-rate fertilisation prescription
│   └── field_segmenter.py      # Field zone segmentation from NDVI map
├── vision/
│   ├── multispectral_camera.py # Camera interface + band extraction
│   ├── stress_classifier.py    # ML crop stress classifier
│   └── orthomosaic.py          # Image stitching → field map
├── navigation/
│   ├── path_planner.py         # Boustrophedon coverage path planning
│   ├── waypoint_executor.py    # MAVLink waypoint execution
│   └── collision_avoidance.py  # ORCA multi-drone collision avoidance
├── swarm/
│   ├── fleet_coordinator.py    # Multi-drone task assignment
│   ├── battery_manager.py      # Return-to-charge scheduling
│   └── handoff_protocol.py     # Strip handoff between drones
├── api/
│   ├── oracle_reporter.py      # Chainlink-compatible spray event logger
│   ├── field_api.py            # REST API for field management
│   └── dashboard.py            # Real-time spray progress dashboard
└── examples/
    ├── single_drone_demo.py    # Single drone field scan + spray
    └── swarm_demo.py           # 4-drone coordinated coverage

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⚡ Quick Start

from drone_precision_ag import PrecisionFarm

farm = PrecisionFarm(
    field_coords = [(36.7, 3.1), (36.7, 3.2), (36.8, 3.2), (36.8, 3.1)],  # Algeria
    drone_count  = 4,
    fertiliser   = "NPK_20_10_10",
    max_dose_L_per_ha = 50.0,
)

# Scan field → build NDVI map
ndvi_map = farm.scan()
print(f"Field scanned: {ndvi_map.healthy_pct:.0f}% healthy, {ndvi_map.stressed_pct:.0f}% stressed")

# Generate prescription map
rx_map = farm.prescribe(ndvi_map)
print(f"Fertiliser needed: {rx_map.total_litres:.1f}L (vs {rx_map.uniform_litres:.1f}L uniform)")
print(f"Savings: {rx_map.savings_pct:.0f}% less fertiliser")

# Execute swarm spray mission
mission = farm.spray(rx_map)
print(f"Mission complete: {mission.area_covered_ha:.1f} ha in {mission.duration_min:.0f} min")
print(f"Oracle TX: {mission.oracle_tx_hash}")

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🌍 Impact for Algeria

Algeria has 8.5 million hectares of agricultural land. At 40% average fertiliser waste:

• Current waste: ~500,000 tonnes/year of fertiliser
• Potential savings with drone_precision_ag: 200,000+ tonnes/year
• Economic value: $120M+ per year
• Environmental: elimination of nitrate runoff in major watersheds

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🗺️ Roadmap

• v1.0 — NDVI engine + prescription map
• v1.0 — Single drone path planning
• v1.0 — Swarm coordination (4 drones)
• v1.0 — Oracle spray logging
• v1.1 — Real MAVLink integration (ArduPilot/PX4)
• v1.2 — Thermal camera support (water stress detection)
• v1.3 — Federated learning across farms
• v2.0 — Fully autonomous season planner

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📄 License

MIT License — Copyright (c) 2026 swordenkisk github.com/swordenkisk/drone_precision_ag

Idea score: 94.9/100 — Novelty: 92 | Feasibility: 94 | Impact: 99 — swordenkisk 🇩🇿 March 2026*