💧 smart_irrigation_mesh

Smart Irrigation Mesh Network — Federated ML Water Optimisation

Low-power IoT sensor mesh — soil moisture + weather + crop model — zero water waste

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🌍 The Problem

Agriculture consumes 70% of all global fresh water — and most is wasted.

• Traditional irrigation: fixed schedule, uniform zones, no feedback
• A farmer irrigates at 6am every day — regardless of last night's rain
• Result: 40–60% of irrigation water evaporates, runs off, or over-saturates roots
• In Algeria alone: 7 billion m³/year wasted on inefficient irrigation
• The Sahara aquifer is being depleted at 10× its recharge rate

The root cause: Farmers have no real-time data on what the soil actually needs.

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✅ The Solution

A self-organising mesh network of low-cost soil sensors that:

1. Senses — each node reads soil moisture, temperature, salinity, pH every 15 min
2. Communicates — LoRaWAN gossip protocol propagates readings across the mesh (10km range)
3. Predicts — on-device ML forecasts soil moisture 24h ahead using weather API
4. Decides — irrigation controller opens/closes valves per zone, per need
5. Learns — federated learning: each farm improves the shared model without sharing raw data

Soil sensor mesh (LoRaWAN)
        ↓ readings every 15 min
Mesh gateway (edge aggregation)
        ↓ fused field map
ML forecaster (soil moisture 24h ahead)
        ↓ irrigation schedule
Valve controller (zone-by-zone)
        ↓ feedback
Federated model update (privacy-preserving)

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🧮 Core Algorithms

Soil Moisture Model (Bucket Model)

dθ/dt = P(t) + I(t) - ET₀(t)·Kc - D(t)

  θ    = volumetric soil moisture [m³/m³]
  P(t) = precipitation rate
  I(t) = irrigation rate (what we control)
  ET₀  = reference evapotranspiration (Penman-Monteith)
  Kc   = crop coefficient (wheat=1.15, tomato=1.05, date palm=0.9)
  D(t) = deep drainage (when θ > field capacity)

Penman-Monteith Evapotranspiration

ET₀ = [0.408·Δ·(Rn-G) + γ·(900/(T+273))·u₂·(eₛ-eₐ)] / [Δ + γ·(1+0.34·u₂)]

  Rn = net radiation, G = soil heat flux
  T  = air temperature, u₂ = wind speed at 2m
  eₛ = saturation vapour pressure
  eₐ = actual vapour pressure
  Δ  = slope of vapour pressure curve
  γ  = psychrometric constant

Irrigation Decision Rule

Trigger:  θ(t) < θ_threshold(crop, growth_stage)
Volume:   I* = (θ_fc - θ(t)) × D_root × A_zone
Cutoff:   stop when θ(t+Δt_predicted) ≥ θ_fc × 0.9

Federated Learning (FedAvg)

Round r:
  Each farm k computes: Δwₖ = ∇L(wᵣ, Dₖ)   (local gradient)
  Server aggregates:    wᵣ₊₁ = Σₖ (nₖ/n) · (wᵣ - η·Δwₖ)
  Privacy: only Δwₖ is shared — raw data Dₖ never leaves the farm

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🏗️ Architecture

smart_irrigation_mesh/
├── core/
│   ├── sensor_node.py         # SensorNode — soil readings + LoRaWAN tx
│   ├── mesh_network.py        # Mesh topology + gossip routing
│   └── irrigation_controller.py # Valve control + decision logic
├── sensors/
│   ├── soil_model.py          # Bucket model — soil moisture dynamics
│   └── et0_calculator.py      # Penman-Monteith ET₀ computation
├── ml/
│   ├── moisture_forecaster.py # 24h soil moisture forecast (LSTM-lite)
│   └── federated_learner.py   # FedAvg — privacy-preserving farm learning
├── mesh/
│   ├── lorawan_sim.py         # LoRaWAN packet simulation
│   └── gossip_protocol.py     # Epidemic mesh propagation
└── examples/
    └── algeria_farm_demo.py   # Full Algeria farm simulation

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⚡ Quick Start

from smart_irrigation_mesh import IrrigationMesh

mesh = IrrigationMesh(
    farm_name   = "Ferme Mitidja — Algeria",
    area_ha     = 50.0,
    crop        = "wheat",
    n_nodes     = 20,           # sensor nodes
    lat, lon    = 36.5, 3.1,    # GPS coordinates
)

# Deploy mesh — nodes self-organise
mesh.deploy()
print(f"Mesh coverage: {mesh.coverage_pct:.0f}%")
print(f"Node density : {mesh.nodes_per_ha:.1f} nodes/ha")

# Run one day of smart irrigation
for hour in range(24):
    state = mesh.tick(hour)
    if state.irrigation_triggered:
        print(f"Hour {hour}: Irrigation zone {state.zone} — {state.volume_m3:.1f} m³")

# Season summary
report = mesh.season_report()
print(f"Water used      : {report.water_used_m3:.0f} m³")
print(f"Water saved     : {report.water_saved_pct:.0f}% vs traditional")
print(f"Yield estimate  : {report.yield_estimate_t_ha:.1f} t/ha")

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🌍 Impact for Algeria

Metric	Value
Agricultural area	8.5 million ha
Current water waste	~7 billion m³/year
Expected savings with mesh	40–55% reduction
Water saved	3–4 billion m³/year
Economic value (at $0.05/m³)	$150–200M/year
Aquifer depletion rate reduction	Significant in Mitidja, Souf, Saoura

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🗺️ Roadmap

• v1.0 — Soil bucket model + Penman-Monteith ET₀
• v1.0 — LoRaWAN mesh simulation (20 nodes)
• v1.0 — Irrigation decision engine
• v1.0 — 24h moisture forecaster
• v1.0 — Federated learning (FedAvg)
• v1.1 — Real LoRaWAN hardware (RAK Wireless / Dragino)
• v1.2 — Satellite ET₀ (Sentinel-2 integration)
• v1.3 — Arabic SMS alerts for farmers
• v2.0 — National mesh (Algeria Ministry of Agriculture API)

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📄 License

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

Idea score: 89.0/100 — Novelty: 97 | Feasibility: 79 | Impact: 97 From ideas_database.db — swordenkisk 🇩🇿 March 2026