AGRIFARM-AI: Intelligent Management System for Climate-Resilient Smart Agriculture

Project Overview

AGRIFARM-AI is an ambitious research project developing an Intelligent Management System (IMS) for climate-resilient smart agriculture. The system integrates cutting-edge technologies to enable autonomous precision farming in controlled environments.

Key Innovation Areas

System Architecture

Core IMS Components

The system extends our proven IMS architecture (ACM MobiCom 2023) with four specialized agricultural agents:

🔄 Agri-IHC (Intelligent Handover Controller)

• Function: Seamless VLC/RF network transitions
• Agricultural Adaptation: VLC-priority in grow zones; RF for mobile robots
• Target: <250ms handover latency

📍 Agri-ETL (Edge Tracking & Localization)

• Function: Federated learning-based positioning
• Agricultural Adaptation: Drone/robot tracking; worker localization via WiFi Doppler
• Target: <30cm accuracy in greenhouse

💧 Agri-ARS (Adaptive Resource Management)

• Function: ML-based resource allocation
• Agricultural Adaptation: Precision irrigation scheduling; sensor prioritization
• Target: 40% water savings, 30% fertilizer reduction

⚡ Agri-EHM (Energy Harvesting Management)

• Function: LED brightness optimization
• Agricultural Adaptation: Dual-purpose grow lights (PAR + VLC)
• Target: 25% energy cost reduction

15-Dimensional Agricultural State Space

Our MARL agents operate on an expanded state space validated with agronomic literature:

Environmental Variables (5)

SoilMoisture       [0-100%]     Volumetric water content
AirTemperature     [15-40°C]    Metabolic rate driver
RelativeHumidity   [20-100%]    Disease pressure indicator
SolarRadiation     [0-1000 W/m²] Photosynthesis driver
CO2Concentration   [400-1000 ppm] CEA optimization

Crop State Variables (4)

GrowthStage        [BBCH 0-9]   Seedling → Harvest
LeafAreaIndex      [0-8 m²/m²]  Canopy development
CropStressIndex    [0-1]        NDVI-derived stress
DaysToHarvest      [0-120]      Countdown timer

Soil Variables (3)

SoilpH             [4-9]        Nutrient availability
ElectricalConductivity [0-8 dS/m] Salinity indicator
NPKLevel           [Low/Med/High] Composite nutrient index

Network Variables (3)

RSSIcurrent        [-90 to -30 dBm] Signal strength
APType             [VLC=1, RF=0]    Access point type
SensorLoad         [0-100%]         Network utilization

Climate Resilience Causal Chain

Complete pathway from forecast to validated outcome:

┌─────────────────┐
│ 1. Data Collection │ IoT sensors + ERA5 climate data
│    (Real-time)      │ Every 15 minutes
└────────┬────────┘
         ▼
┌─────────────────┐
│ 2. Prediction      │ SEACLID regional model + LLM
│    (48-72 hours)   │ Heat stress, drought, pest risk
└────────┬────────┘
         ▼
┌─────────────────┐
│ 3. Decision        │ LLM + RAG knowledge base
│    Support         │ "Reduce irrigation 20% now"
└────────┬────────┘
         ▼
┌─────────────────┐
│ 4. Action          │ IMS-ARS automated execution
│    Execution       │ OR farmer SMS notification
└────────┬────────┘
         ▼
┌─────────────────┐
│ 5. Outcome         │ Treatment vs. control plot
│    Validation      │ Quantified yield improvement
└─────────────────┘

MARL Coordination Mechanism

Agent Interaction Model

Coordination: Centralized Training, Decentralized Execution (CTDE)

Value Decomposition: QMIX algorithm

Convergence: Nash equilibrium via fictitious play (<1000 episodes)

Reward Functions

Federated Learning Framework (AgriFL)

Privacy-preserving cross-border agricultural intelligence:

Data Schema

Privacy Guarantees

• Differential Privacy: ε ≤ 0.5
• Secure Aggregation: No raw data leaves farm
• Cluster-based FL: Tropical (ASEAN) + Temperate (EU)

Validation Plan

Target KPIs

Technology Stack

Hardware

• Edge Gateways: Jetson Nano (<50ms inference)
• VLC Platform: OpenVLC 1.3 + BeagleBone Black
• Sensors: Low-cost nodes (€50/unit vs. €200+ commercial)

Software

• MARL Framework: QMIX with PyTorch
• Foundation Model: LLaMA-3.1-8B (5M agricultural tokens)
• FL Framework: AgriFL with Byzantine-robust aggregation
• Climate APIs: ERA5, SEACLID

Development Timeline

2026        2027        2028        2029
  │           │           │           │
  ├──Phase 1──┼──Phase 2──┼──Phase 3──┼──Phase 4──┤
  │           │           │           │           │
  │ Design    │ Integration│ Validation│ Transfer  │
  │ Farmer    │ MARL Train │ 4 Trials │ Open-Source│
  │ Co-Design │ Edge AI    │ Impact   │ Commercial │
  └───────────┴───────────┴──────────┴───────────┘

Publications & Resources

Related Publications

• ACM MobiCom 2023: IMS Architecture for Hybrid VLC/RF
• LNEE 2024: Offline RL for Agricultural IoT (90.4% success)
• IEEE ICCC: VLC/WiFi MAC Optimization

Links

• 📂 GitHub: https://github.com/kotobuki09/AGRIFARM-AI
• 📄 Documentation: Under development