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# 🌾 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
```bash
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
```bash
git clone https://github.com/your-username/smartfarm.git
cd smartfarm
```
### 2. Install Python Dependencies
```bash
pip install -r requirements.txt
```
### 3. Set Up Ollama
Make sure you have Ollama installed and running.
Download the LLaMA 3 model:
```bash
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:
```json
{
"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:
```bash
data/docs/
```
The system will automatically build a searchable vector index.
---
## ▶️ Usage
### 📟 Command-Line Mode
```bash
python main.py
```
You’ll be prompted to enter queries like:
```markdown
> 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:
```bash
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
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