A powerful Flutter-based smart agriculture management app that helps farmers optimize fertilizer usage, monitor crops, and control irrigation remotely. The app provides real-time sensor data, weather forecasts, and AI-powered recommendations.

Immanuel Jeyam

Jijin Jebanesh

• 🏠 Home Dashboard

  • Live sensor data visualization (soil moisture, temperature, humidity)
  • Current weather and 24-hour forecast
  • Quick access to motor control

  

• ⚡ Real-time Optimization

  • One-click fertilizer optimization using ML model
  • NPK level recommendations

  

• 🤖 Gemini AI-Powered Analysis

  • 💬 Chat with a Gemini-powered AI bot for instant answers
  • 🌾 Get contextual suggestions based on real-time weather and soil data
  • 🗣️ Supports multiple regional languages for enhanced accessibility
  • 🧠 AI learns from inputs and provides accurate insights

  

• 🌦 Weather Forecast

  • 7-day weather forecast with remarks
  • Weather alerts and recommendations

  

• 🚰 Irrigation Control

  • Remote motor on/off control
  • Automatic scheduling based on soil conditions

  

• 📊 Analytics Dashboard

  • Live Sensor graph

  

• 💬 Multi-language Chat Support

  • Gemini-powered chat interface
  • Regional language support for farmers

  

• 👤 Field Management

  • Switch between multiple fields
  • View field-specific sensor data

  

• ⚙️ Settings Page

  • 👤 View and manage your user profile
  • 🔔 Toggle Email Alerts for critical updates
  • 📲 Enable or disable Push Notifications
  • 💧 Switch Remote Motor Control Feature on/off
  • 🚪 Use the Logout Button to securely sign out

• 🔧 Flutter (Mobile App)
• 🐍 Python (ML Backend)
• 🌐 Node.js + Express.js (Backend)
• 🔥 MongoDB (Realtime Database)
• 🤖 Gemini API (Analysis & Chat)
• 🌦 WeatherAPI (Forecast Data)

├── API
│   ├── api.dart
│   ├── mlApi.dart
│   └── weather_service.dart
├── constants
│   ├── buttonOutlined.dart
│   ├── image_strings.dart
│   ├── page_indicator.dart
│   ├── sizes.dart
│   ├── spacing.dart
│   └── text_strings.dart
├── helper_functions
│   └── helper_functions.dart
├── main.dart
├── models
│   ├── ferilizer_model.dart
│   ├── nutritionData_model.dart
│   ├── sensorData_model.dart
│   ├── weatherData_model.dart
│   └── weather_model.dart
├── pages
│   ├── analytics2.dart
│   ├── analytics.dart
│   ├── home.dart
│   ├── login.dart
│   ├── settings.dart
│   └── week_screen.dart
├── popups
│   ├── fullscreen_loaders.dart
│   └── loaders.dart
├── providers
│   └── analyticsProvider.dart
├── routes.dart
├── theme
│   ├── custom_theme
│   │   └── elevated_button_theme.dart
│   └── theme.dart
└── utils
    ├── adBox.dart
    ├── analysis_box.dart
    ├── appbar.dart
    ├── CategoryItems.dart
    ├── CFC.dart
    ├── dataContainer.dart
    ├── device_utils.dart
    ├── home_container.dart
    ├── linechart.dart
    ├── npk_box.dart
    ├── productBox_L.dart
    ├── productBox_s.dart
    ├── sensor_data_box.dart
    ├── settingContainer.dart
    ├── sharedPrefs.dart
    ├── shimmerWidget.dart
    ├── TC.dart
    ├── TFD.dart
    └── weatherContainer.dart

The backend is built using Node.js with Express.js, handling:

• Sensor Data Processing

  • REST endpoints for receiving sensor data
  • WebSocket support for real-time updates
  • Data validation and sanitization

• Machine Learning Integration

  • Python child processes for ML model execution
  • API endpoints for fertilizer recommendations
  • Crop-specific prediction models

• Weather Data Integration

  • Weather API proxy endpoints
  • Forecast data caching
  • Weather alert system

• Device Control

  • IoT device management endpoints
  • Motor control API
  • Irrigation scheduling system

• Authentication & Security

  • JWT authentication
  • Role-based access control
  • API rate limiting

git clone https://github.com/IMMANUEL-88/ferti_optimizer.git
cd ferti_optimizer
flutter pub get
flutter run

Note: While you can clone and run the app locally, please be aware that:

• Some backend APIs are currently hosted on a local server (localhost), and others are hosted live.
• To experience full functionality (ML predictions, sensor data sync, motor control, etc.), you’ll need to set up the backend environment locally or deploy it to a live server.
• The necessary environment variables (.env) are maintained privately and are not included in the repository for security reasons.