🌾 AgriEdge: Smart Farm Assistant
An AI-powered assistant that uses real-time sensor data and textbook-based agricultural knowledge to provide insights, analysis, and actionable suggestions for small to medium-scale farms. Comes with both command-line and web interfaces.
🚀 Features
- 📡 Analyzes real-time farm sensor data (soil, water, environment)
- 📚 Retrieves context from agricultural PDF documents
- 🤖 Uses retrieval-augmented generation (RAG) for grounded reasoning
- 🧠 Powered by Ollama + LLaMA 3
- 📝 Generates natural language summaries and actionable insights
- 🔒 Runs fully local — no cloud, no data sharing
- 🌐 Supports Streamlit-based dashboard for non-technical users
📁 Project Structure
smartfarm/
├── main.py # Command-line interface
├── app.py # Streamlit web app
├── llm/
│ ├── ollama_llm.py # Query handler using LLM + sensor data + RAG
│ └── rag_pipeline.py # PDF retrieval pipeline using FAISS
├── logger.py # Logging setup
├── prompt.txt # Prompt template for LLM
├── data/
│ ├── farm_data_log.json # JSON file logging sensor readings
│ ├── docs/ # Agricultural PDFs for knowledge retrieval
│ └── faiss_index/ # Auto-generated FAISS vector index
🛠️ Installation & Setup
1. Clone the Repository
git clone https://github.com/your-username/smartfarm.git
cd smartfarm
2. Install Python Dependencies
pip install -r requirements.txt
3. Set Up Ollama
Make sure you have Ollama installed and running.
Download the LLaMA 3 model:
ollama run llama3
Make sure Ollama is running in the background before using the assistant.
4. Add Sensor Data
Append new entries to data/farm_data_log.json. Example format:
{
"timestamp": "2025-07-22T21:00:00+01:00",
"soil": {"moisture": "High", "pH": 6.8, "temperature": 24.9},
"water": {"pH": 7.2, "turbidity": "8 NTU", "temperature": 23.3},
"environment": {"humidity": "85%", "temperature": 26.0, "rainfall": "Moderate"}
}
5. Add Agricultural Documents (Optional)
Place your farming-related PDFs inside:
data/docs/
The system will automatically build a searchable vector index.
▶️ Usage
📟 Command-Line Mode
python main.py
You’ll be prompted to enter queries like:
> Is the soil suitable for planting now?
> Has the turbidity improved compared to earlier?
Type exit to quit.
🌐 Streamlit Web Interface
Launch the UI with:
streamlit run app.py
What you can do:
- View the most recent sensor snapshot
- Ask farm-related questions like:
- "What is the current soil condition?"
- "Is it safe to irrigate now?"
- "Has rainfall increased compared to earlier?"
💡 Notes
- The system analyzes only the most recent sensor reading but uses the previous 2 for historical comparison (internally).
- No internet connection is required once the vector store and model are set up.
- Logs are written automatically to
logs/.
🧪 Example Questions
- "Is the soil moisture improving?"
- "What is the overall environmental condition right now?"
- "Is the water quality good for irrigation?"
📄 License
MIT License