IoT Smart Watering System

Project Overview

This smart watering system automates plant care by monitoring soil moisture and environmental conditions. When the soil gets too dry, it waters the plant on its own and logs everything to a cloud dashboard.

The Challenge

Manual watering fails when owners forget or travel. The design requirements were low cost, autonomous watering, and remote monitoring.

SolidWorks drawing of complete enclosure

System Architecture

Hardware and software components:

Hardware Components

ESP32 Microcontroller: The system brain that processes sensor data and controls the water pump
Capacitive Soil Moisture Sensors: Provide accurate moisture readings without corrosion issues
DHT22 Sensor: Monitors ambient temperature and humidity
Water Pump: Delivers precise amounts of water when needed
Water Reservoir: Stores water for automated dispensing

Software & Connectivity

Arduino IDE: Used for programming the ESP32
Adafruit IO: Cloud platform for data visualization and remote monitoring
IFTTT: Delivers notifications when watering occurs or when water levels are low
Wi-Fi Connectivity: Enables remote access and data transmission

Circuit architecture diagram showing all component connections

How It Works

Moisture sensors continuously monitor soil conditions
When moisture levels drop below the preset threshold (30% in testing), the water pump activates
The DHT22 sensor collects environmental data (temperature and humidity)
All data is sent to Adafruit IO for visualization and analysis
Users receive notifications through IFTTT when watering occurs
The system operates autonomously but allows for manual overrides

System block diagram of the moisture sensor and pump assembly

Development Process

Development process:

Requirements Analysis: Identified key functionality needed for effective plant care
Component Selection: Chose reliable, affordable components suitable for the application
Prototyping: Built and tested basic circuits for each sensor and the pump mechanism
Software Development: Created firmware for the ESP32 including sensor reading, pump control, and data transmission
Cloud Integration: Established connections to Adafruit IO and set up data feeds
Testing and Refinement: Tested across a range of soil and temperature conditions

Performance Results

Test results:

Moisture Detection: Accurate readings within ±3% compared to manual measurements
Watering Precision: Consistent delivery of water when moisture dropped below 30%
Data Reliability: 98% successful data transmission rate to cloud platform
Power Efficiency: Low power consumption suitable for potential battery operation
Notification System: Timely alerts with no false positives during testing period

Graph showing moisture levels and watering events over time from Adafruit IO dashboard

Planned Improvements

Identified next steps:

Solar power: A small panel would remove the need for wall power outdoors.
Water level sensing: A level sensor would detect low water in the reservoir.