🌱 AgriSense — Smart Agriculture Platform

A full-stack smart agriculture platform combining IoT soil monitoring, AI-powered crop disease detection, and an intelligent agricultural chatbot — all in one unified system.

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🌟 Key Features

📡 IoT Soil Monitoring

• Real-time Monitoring: Live soil moisture readings continuously synced from IoT sensors via Firebase.
• 📊 Beautiful Dashboard: Animated circular gauges, live updating line charts, and dynamic status indicators.
• 🔔 Smart Alerts: Instant notifications triggered when moisture drops to critical levels.
• 🎨 3D Interactive UI: Stunning Three.js animations and GSAP scroll effects for an immersive experience.

🔬 Crop Disease Detection

• 📷 AI Image Analysis: Upload a photo of any plant leaf and get an instant disease diagnosis powered by Gemini 2.5 Flash.
• 🩺 Detailed Diagnosis: Identifies the disease name, estimates severity (0–100%), and provides a practical cure/recommendation.
• ✅ Confidence Scoring: Each result includes an AI confidence score for transparency.
• 🛡️ Non-Crop Guard: Automatically rejects non-plant images with a helpful error message.

🤖 AgriBot Chatbot

• 💬 Agricultural Expert AI: Ask any farming or crop-related question and receive structured, expert-level answers.
• 🌐 Google Search Grounding: Responses are enriched with real-time web data for up-to-date agricultural advice.
• 🔊 Text-to-Speech: Built-in browser TTS reads out responses for hands-free use in the field.
• 📖 Structured Responses: Answers are always formatted with an [OVERVIEW] and [KEY FINDINGS] section for clarity.

📱 General

• Responsive Design: Seamlessly adapts to both desktop and mobile devices.
• ⚡ Firebase Integration: Robust real-time database ensuring rapid and seamless data synchronization.

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📷 IoT Hardware Sensor

Capacitive Soil Moisture Sensor (ESP32-based) deployed in a crop field

🔧 Components Needed

• ESP32 or ESP8266 microcontroller board
• Capacitive Soil Moisture Sensor v1.2
• Jumper wires and Breadboard
• Power supply (USB or battery bank)

🔌 Wiring Diagram

Sensor Pin	ESP32 Connection
VCC	3.3V
GND	GND
AOUT	GPIO34 (ESP32) or A0 (ESP8266)

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📸 Interface Preview

🏠 Home Page — 3D Interactive Landing

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📊 Real-Time Dashboard

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📁 Project Structure

AgriSense/
├── assets/                        # Images and media assets for this README
│
├── backend/                       # Node.js REST API & Firebase integration
│   ├── routes/                    # Express endpoints (moisture.js)
│   ├── utils/                     # Moisture logic & thresholds
│   └── server.js                  # Main entry point
│
├── backend_python/                # Python FastAPI server (AI features)
│   ├── server.py                  # AgriBot chat & disease detection API
│   ├── requirements.txt           # Python dependencies
│   └── .env                       # Gemini API key configuration
│
├── frontend/                      # Client-side web application
│   ├── index.html                 # Landing page with 3D elements
│   ├── dashboard.html             # Live soil analytics dashboard
│   ├── disease_detector.html      # 🔬 AI crop disease detection page
│   ├── chatbot.html               # 🤖 AgriBot agricultural chatbot page
│   ├── css/                       # Styling & themes
│   └── js/                        # Three.js, GSAP, and Chart.js logic
│
├── iot/                           # Microcontroller code & testing tools
│   ├── esp32_moisture_sensor.ino  # Arduino firmware
│   └── data_simulator.js          # Mock data generator (for testing)
│
└── docs/                          # Additional documentation

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🚀 Quick Start Guide

Prerequisites

• Node.js (v16 or higher)
• Python (v3.10 or higher)
• A Firebase account for real-time data
• A Google Gemini API Key for AI features
• Arduino IDE (if flashing the IoT firmware)

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⚙️ Part 1: IoT Monitoring Backend (Node.js)

1. Clone & Install

git clone https://github.com/Umesh-369/AgriSense.git
cd AgriSense/backend
npm install

2. Configure Firebase

1. Create a Firebase project and enable the Realtime Database.
2. Go to Project Settings → Service Accounts and generate a new private key.
3. Save the file as backend/serviceAccountKey.json.
4. Copy the environment variables example and update your database URL:

cp .env.example .env

FIREBASE_DATABASE_URL=https://your-project-id.firebaseio.com

3. Start the Node.js Server

npm start

Note: Without Firebase configuration, the app runs in Demo Mode with simulated data.

Open in browser:

• 🏠 Home: http://localhost:3000
• 📊 Dashboard: http://localhost:3000/dashboard

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🤖 Part 2: AI Features Backend (Python / FastAPI)

This server powers both the Crop Disease Detector and the AgriBot Chatbot.

1. Set Up Python Environment

cd AgriSense/backend_python
python -m venv .venv
.venv\Scripts\activate      # Windows
# source .venv/bin/activate # macOS/Linux
pip install -r requirements.txt

2. Configure Gemini API Key

Create a .env file inside backend_python/ with your key:

GEMINI_API_KEY=your_gemini_api_key_here

You can get a free API key from Google AI Studio.

3. Start the Python Server

python server.py

The AI server will be available at http://localhost:8000.

Open the AI-powered pages:

• 🔬 Disease Detector: Open frontend/disease_detector.html in your browser
• 🤖 AgriBot Chatbot: Open frontend/chatbot.html in your browser

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📊 API Endpoints Reference

Node.js API (Port 3000)

Endpoint	Method	Description
/api/health	GET	Verifies server operational status
/api/moisture/current	GET	Fetches the latest moisture reading
/api/moisture/history	GET	Retrieves historical data (past 24h)
/api/moisture/status	GET	Calculates current status and alerts
/api/moisture/thresholds	GET	Returns threshold configuration data

Python FastAPI (Port 8000)

Endpoint	Method	Description
/api/health	GET	Verifies AgriBot server status
/api/chat	POST	Sends a message to the AgriBot chatbot
/analyze	POST	Uploads a crop image for disease analysis

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🔬 Crop Disease Detector — How It Works

1. Upload a clear photo of a plant leaf via the disease_detector.html page.
2. The image is sent to the Python FastAPI /analyze endpoint.
3. Gemini 2.5 Flash analyzes the image as an expert plant pathologist.
4. The result is returned as a structured JSON with:
   • disease — The identified disease name (or "Healthy")
   • severity — A percentage score (0–100%)
   • cure — A concise, practical recommendation
   • confidence — The AI's confidence in the diagnosis

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🤖 AgriBot Chatbot — How It Works

1. Ask any agriculture-related question via the chatbot.html page.
2. The message is sent to the Python FastAPI /api/chat endpoint.
3. Gemini 2.5 Flash responds with Google Search grounding enabled for real-time accuracy.
4. Responses are always structured as:
   • [OVERVIEW] — A single summary sentence
   • [KEY FINDINGS] — 3–5 concise bullet points with bolded key terms
5. Optional Text-to-Speech reads the response aloud via the browser's Web Speech API.

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💻 IoT Hardware Setup Details

1. Open iot/esp32_moisture_sensor.ino in your Arduino IDE.
2. Install the Firebase ESP Client library by Mobizt via the Library Manager.
3. Update credentials:

   #define WIFI_SSID      "your_wifi_name"
   #define WIFI_PASSWORD  "your_wifi_password"
   #define FIREBASE_HOST  "your-project.firebaseio.com"
   #define FIREBASE_AUTH  "your_firebase_secret"

4. Flash the code to your ESP32/ESP8266 board.

🧪 Testing Without Hardware

Use the included data simulator to generate realistic mock readings:

cd iot
node data_simulator.js

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🎨 Technologies & Stack

Category	Technologies Used
Frontend UI/UX	HTML5, CSS3, JavaScript (ES6+), Glassmorphism
3D & Animation	Three.js, GSAP (GreenSock)
Data Visualization	Chart.js
IoT Backend & API	Node.js, Express.js
AI Backend & API	Python, FastAPI, Uvicorn
AI / LLM	Google Gemini 2.5 Flash (google-genai SDK)
Database	Firebase Realtime Database
IoT / Hardware	C++ (Arduino), ESP32 / ESP8266

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🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to open an issue or submit a pull request.

📄 License

This project is MIT licensed.

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Designed & Developed with ❤️ for Smart Agriculture