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WaterIT

Precision watering, data-driven insights, and expert recommendations.

WaterIT
 
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Hardware Components

  • Arduino UNO

    X 1 fenxiang
  • Crowtail soil moisture sensors

    X 1 fenxiang
  • 5V Relay module

    X 1
  • Crowtail Water Pump

    X 1 fenxiang
  • Water pipes

    X 1
  • Rechargeable Battery, Lithium Ion

    X 1
  • SG-90 Servo Motor

    X 1 fenxiang
  • other auxiliary materials

    X 1

Tools, APP Software Used etc.

  • VSCode

    Microsoft
  • Arduino IDE

    Arduino IDE

    Arduino
  • Python Programming Language

    Guido van Rossum

Story

 

 

Project Overview

WaterIT is a smart, automated plant watering system built using Flask, Arduino, and AI. It intelligently monitors soil conditions and automates irrigation while offering plant care insights through integrated APIs.


Key Components

1. Hardware

  • Arduino UNO – Controls sensors and pump

  • Soil Moisture Sensor – For real-time monitoring

  • Water Pump + Relay – Manages irrigation

  • Servo Motor – Provides mechanical feedback

2. Software Stack

  • Flask – Backend web application

  • SQLite – Lightweight database for storage

  • Gemini API (Google Generative AI) – Offers plant care advice

  • OpenWeatherMap API – Integrates live weather data

  • Plant.id API – Identifies plants via uploaded images

3. Core Features

  • Real-time moisture sensing & auto-watering

  • AI-powered plant care recommendations

  • Interactive data visualization

  • Plant identification from images

  • Weather-aware irrigation


How It Works

1. Moisture Monitoring

// Arduino code monitors soil moisture
int moistureLevel = 1023 - analogRead(sensorPin);
if (moistureLevel < 400 && !pumpOn) {
  digitalWrite(pumpPin, LOW);  // Activate pump
}

2. Data Collection

def save_moisture_data(moisture_level):
    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    moisture_level = round(moisture_level)
    conn = sqlite3.connect(MOISTURE_DB)
    c = conn.cursor()
    c.execute("INSERT INTO moisture_data VALUES (?, ?)",
            (timestamp, moisture_level))

3. Web Interface

  • Dashboard – Monitor system status and alerts

  • Graphs – Visualize moisture trends

  • AI Chat – Get plant care guidance

  • About Page – System overview and usage


Key Features

1. Automated Watering

  • Moisture threshold control

  • Smart pump activation

  • Water usage tracking

2. Data Analysis

  • Live and historical moisture graphs

  • Trend-based insights

  • Environment and weather context

3. AI Integration

  • AI-based plant identification

  • Personalized care tips

  • Interactive AI assistant

Code
  • Code on GitHub

    https://github.com/ZephyrusBlaze/WaterIT
    View

WaterIT

Precision watering, data-driven insights, and expert recommendations.

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  • I like the way you connected the hardware with a backend server for implementing AI ????
    Jun 15,2025 1 comments
    Reply
  • Could you please help me build a similar project? :)
    Jun 15,2025 2 comments
    Reply

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