🌱 FarmInsight

Smart Monitoring Platform for Food Production Facilities

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📋 Table of Contents

• About
• Features
• Architecture
• Repository Structure
• Quick Start
• Technology Stack
• Feature Showcase
• Contributing
• Contributors
• License

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🎯 About

FarmInsight is a comprehensive open-source platform for monitoring and managing Food Production Facilities (FPFs). Developed at TU Clausthal as part of the Digital Technologies program, it provides real-time sensor data visualization, AI-powered forecasting, and automated control systems.

🌟 Core Vision

"Maximum yield with minimal sustainable resource input"

FarmInsight enables organizations to manage multiple Food Production Facilities (FPFs) – from vertical farms to greenhouses – with precision and ease:

• Monitor – Real-time sensor data from temperature, humidity, soil moisture, and more
• Analyze – Interactive visualizations with historical trends and custom date ranges
• Predict – AI-powered forecasts for water and energy consumption
• Automate – Smart triggers and controllable actions for lights, pumps, and climate
• Collaborate – Multi-tenant architecture with role-based access (Admin/Member)

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✨ Features

🏢 Organization Management Multi-tenant architecture Role-based access (Admin/Member) Assign FPFs to organizations 📊 Sensor Integration HTTP & MQTT protocols Configurable polling intervals Real-time data streaming via WebSocket 10+ sensor models supported 📷 Camera Support Scheduled image capture RTSP/HTTP livestreaming Historical image gallery

🤖 AI-Powered Forecasting Water level predictions with irrigation optimization Energy consumption forecasts Best/Average/Worst-case scenarios Weather data integration (Open-Meteo) ⚡ Smart Automation Trigger-based actions (interval, time, sensor threshold) Hardware control via network (HTTP) Queue-based execution with hardware protection Manual override capability 🔔 Notifications Matrix server integration Configurable alerts per FPF Real-time status updates

💧 Water Management Dashboard Animated tank fill level visualization Field moisture monitoring AI-driven irrigation scheduling (Best/Average/Worst) Proactive refill recommendations

⚡ Energy Management Dashboard Battery State-of-Charge monitoring Solar production & grid power tracking Multi-scenario forecasts (Expected/Optimistic/Pessimistic) Automated grid connect/disconnect thresholds

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🏗 Architecture

flowchart TB
    subgraph Client["Client"]
        Browser["Web Browser"]
    end

    subgraph Frontend["Dashboard Frontend"]
        React["React + Redux + Mantine UI"]
    end

    subgraph Backend["Dashboard Backend"]
        Django1["Django REST API"]
        SQLite1[(SQLite)]
        InfluxDB[(InfluxDB)]
        Scheduler1["Task Scheduler"]
    end

    subgraph AI["AI Backend"]
        Django2["Django ML Service"]
        Models["Trained Models"]
        Weather["Open-Meteo API"]
    end

    subgraph FPF["FPF Backend"]
        Django3["Django Sensor Service"]
        SQLite2[(SQLite)]
        Scheduler2["Data Collector"]
    end

    subgraph Hardware["Hardware Layer"]
        Sensors["Sensors"]
        Cameras["Cameras"]
        Actuators["Smart Plugs"]
    end

    Browser <-->|HTTP| React
    React <-->|REST + WebSocket| Django1
    Django1 <--> SQLite1
    Django1 <--> InfluxDB
    Django1 <-->|REST| Django2
    Django2 --> Models
    Django2 <-->|REST| Weather
    Django1 <-->|REST| Django3
    Django3 <--> SQLite2
    Django3 --> Scheduler2
    Scheduler2 <-->|HTTP / MQTT| Sensors
    Scheduler2 <-->|HTTP / RTSP| Cameras
    Scheduler2 <-->|HTTP / MQTT| Actuators

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🔄 Data Flow

sequenceDiagram
    participant S as 🌡️ Sensor
    participant FPF as 🌱 FPF Backend
    participant DB as ⚙️ Dashboard Backend
    participant AI as 🧠 AI Backend
    participant UI as 📱 Frontend
    participant U as 👤 User

    S->>FPF: Measurement (HTTP/MQTT)
    FPF->>DB: Store data (REST)
    DB->>DB: Save to InfluxDB
    
    loop Every Interval
        DB->>AI: Request Forecast
        AI->>AI: Run ML Model
        AI-->>DB: Predictions + Actions
    end
    
    U->>UI: Open Dashboard
    UI->>DB: Request Data (REST)
    DB-->>UI: Sensor Data + Forecasts
    UI-->>U: Visualize
    
    alt Trigger Activated
        DB->>DB: Execute Action
        DB->>S: Control Hardware
    end

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📁 Repository Structure

FarmInsight/
├── 📱 dashboard-frontend/     # React Web Application
│   ├── smart_farm_frontend/   # Source code
│   └── .documentation/        # UI screenshots & guides
│
├── ⚙️ dashboard-backend/       # Central Django API
│   ├── django_server/         # Django project
│   ├── Dockerfile             # Container config
│   └── docker-compose.yml     # InfluxDB setup
│
├── 🌱 fpf-backend/             # Sensor Collection Service
│   ├── django_server/         # Django project
│   └── .documentation/        # Arduino/Pi Pico scripts
│
├── 🧠 ai-backend/              # ML Prediction Service
│   ├── model_service/         # Django ML project
│   ├── Dockerfile             # Container config
│   └── docs/                  # Model documentation
│
├── 📄 LICENSE                  # AGPL-3.0
└── 📖 README.md                # This file

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🚀 Quick Start

Prerequisites

Component	Requirement
Node.js	v18+ LTS
Python	v3.11+
pip	v24+
Docker	Optional (for InfluxDB)

Development Setup

1️⃣ Clone the Repository

git clone https://github.com/ETCE-LAB/FarmInsight.git
cd FarmInsight
git submodule init && git submodule update

2️⃣ Start the Dashboard Backend

cd dashboard-backend/django_server

# Create virtual environment
python -m venv .venv
.venv\Scripts\activate  # Windows
# source .venv/bin/activate  # Linux/Mac

# Install dependencies
pip install -r requirements.txt

# Generate OIDC key
openssl genrsa -out rsa/oidc.key 4096

# Setup database
python manage.py migrate
python manage.py loaddata application

# Start server
python manage.py runserver 8000

3️⃣ Start the Frontend

cd dashboard-frontend/smart_farm_frontend

# Install dependencies
npm install

# Configure backend URL
echo 'export const BACKEND_URL = "http://127.0.0.1:8000";' > src/env-config.ts

# Start development server
npm start

4️⃣ (Optional) Start FPF Backend

cd fpf-backend/django_server
pip install -r requirements.txt
python manage.py migrate
python manage.py runserver 8001

5️⃣ (Optional) Start AI Backend

cd ai-backend/model_service
pip install -r requirements.txt
python manage.py runserver 8002

📚 For detailed setup instructions, see the README in each component folder.

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🛠 Technology Stack

Layer	Technologies
Frontend
Backend
Database
AI/ML
IoT

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📸 Feature Showcase

Explore the FarmInsight platform through our modern, intuitive interface designed for efficient farm management.

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🏠 Landing Page & FPF Overview

_{Dashboard overview showing all Food Production Facilities with live camera previews and maintenance status indicators}

The landing page provides instant visibility into all your FPFs:

• Live Camera Previews – Real-time thumbnails from each facility
• Status Indicators – Quick identification of facilities under maintenance
• Organization Labels – Easy filtering by organization (e.g., ETCE)
• Search & Create – Quickly find or add new facilities

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📊 Real-Time Monitoring Dashboard

Dashboard Features 🌡️ Live Sensor Data Interactive graphs with time range selection Smooth value visualization option Multiple sensor overlay support 📷 Camera Integration Live image/stream toggle Timestamped captures Full-screen preview 🌤️ Weather Forecast Current conditions 3-day forecast preview Location-based data 🔌 Hardware Controls On/Off/Auto modes Manual override capability

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⚙️ Sensor Configuration & Management

_{Comprehensive sensor management with real-time status, logging capabilities, and threshold configuration}

📐 10+ Sensor Models	⏱️ Configurable Intervals	🚨 Threshold Alerts	📝 Data Logging
DHT22, SenseCAP, Shelly & more	From seconds to hours	Custom warning levels	Full measurement history

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💧 Water Management Dashboard

Real-Time Monitoring Animated Tank Visualization with live fill level Current Volume Display showing capacity used Water Temperature with freeze warning Soil Moisture Tracking for field sensors

AI-Powered Forecasting 7-Day Water Level Prediction graph Optimal Irrigation Schedule generated automatically Consumption Analytics with daily averages Proactive Refill recommendations

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⚡ Energy Management Dashboard

🔋 Battery Status Live SoC monitoring Configurable capacity Real-time updates

⚡ Power Balance Production vs Consumption Net power calculation Solar tracking

🔌 Grid Status Connection state Auto-connect thresholds Manual override

🚨 Smart Actions Emergency shutdown Load shedding AI-triggered responses

AI Forecast Scenarios

Both Water and Energy dashboards provide multi-scenario predictions:

Scenario	Description	Planning Use
🟢 Best Case	Optimal conditions, minimal consumption	Capacity planning
🔵 Expected	Average conditions based on historical data	Daily operations
🟠 Worst Case	Adverse conditions, high consumption	Risk preparation

Smart Automation: The system can automatically trigger actions (e.g., grid connect, pump activation) based on forecast thresholds.

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🎬 Automation in Action

Demonstration of the trigger system: Manual override → Auto mode → Time-based triggers

🔧 Learn more about Controllable Actions

FarmInsight's automation system supports multiple trigger types:

Trigger Type	Description	Example
⏰ Interval	Repeat at fixed intervals	Water pump every 6 hours
🕐 Time-Based	Execute within timeframe	Lights on 6:00-18:00
📊 Sensor	React to measurements	Fan on if temp > 30°C
👆 Manual	One-click execution	Emergency stop

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🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

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👥 Contributors

This project was developed as part of the Digitalisierungsprojekt at Digital Technologies, TU Clausthal.

Development Team

Tom Luca Heering	Theo Lesser	Mattes Knigge	Julian Schöpe
Marius Peter	Paul Golke	Niklas Schaumann	M. Linke

Supervision

• Johannes Mayer – Project Lead
• Benjamin Leiding – Academic Supervisor

Special Thanks

• Anant – Deployment & Infrastructure

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📄 License

This project is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0).

See the LICENSE file for details.

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Made with 💚 at TU Clausthal
🌐 Live Demo • 📦 ETCE-Lab GitHub