Objective:

To develop a smart farming web-based system that uses AI and IoT technologies to monitor field conditions in real time, analyze agricultural data, and provide intelligent recommendations for improved crop health, irrigation, fertilization, and productivity.

Technologies Used:

• Frontend: HTML, CSS, Bootstrap, JavaScript

• Backend: Node.js / PHP / Java

• Database: MongoDB / MySQL

• AI/ML: Python (for crop prediction and recommendation), integrated via REST APIs

• IoT Integration: Arduino/Raspberry Pi with sensors (DHT11, moisture, pH, etc.)

• Data Visualization: Chart.js or D3.js

Key Features:

1. Sensor-Based Real-Time Monitoring:

   • IoT sensors collect live data on soil moisture, temperature, humidity, light, and pH levels.

   • Data sent to backend using Wi-Fi or GSM modules.

2. AI-Based Crop Recommendation System:

   • Machine Learning model predicts best crops to grow based on current soil conditions and location.

   • Uses historical data for enhanced accuracy.

3. Irrigation and Fertilization Alerts:

   • System recommends optimal watering times.

   • Notifies farmers of ideal fertilization schedules.

4. Disease Prediction and Prevention:

   • Uses image or symptom input to identify plant diseases (optional).

   • Suggests preventive actions or treatments.

5. Dashboard for Farmers:

   • Visualizes real-time field data and trends.

   • Tracks historical performance and alerts.

   • Accessible from desktop or mobile.

6. Weather API Integration:

   • Shows real-time and forecasted weather data to inform decisions.

7. Smart Alert System:

   • SMS/email alerts for unusual sensor readings (e.g., low moisture, high temperature).

8. Data Logging & Analytics:

   • Stores data for long-term analysis and crop planning.

   • Displays trends over weeks/months.