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Self Water Plant System

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PROJECT OVERVIEW:

This individual project demonstrates my ability to integrate hardware automation with embedded programming to solve a real-world problem plant irrigation management. I designed and developed an Arduino-based Self-Watering Plant System that automatically detects soil moisture levels and activates a water pump when the soil becomes dry.

The system combines a Soil Moisture Sensor, Arduino UNO, Relay Module, and DC Motor (Pump) to create a fully autonomous watering cycle. By continuously reading soil moisture values through the sensor, the Arduino intelligently controls the relay to power the pump only when needed, preventing both overwatering and water waste.

This project highlights my skills in sensor calibration, analog data acquisition, circuit design, and embedded C programming, while also demonstrating my ability to design efficient and sustainable systems for real-world use.

TECHNICAL IMPLEMENTATION:

  • Hardware Integration: Designed a fully functional circuit connecting the Soil Moisture Sensor, Relay Module, and DC Pump Motor to an Arduino UNO, ensuring proper voltage isolation and grounding.

  • Signal Processing: Converted analog moisture sensor signals (0–1023) into calibrated digital decisions for relay actuation using ADC (Analog-to-Digital Conversion) principles.

  • Control Algorithm: Implemented a closed-loop control system with real-time threshold comparison to dynamically maintain soil moisture within the optimal range.

  • Relay Driver Design: Configured a 5V single-channel relay to switch higher-voltage DC loads safely using transistor-based switching and flyback diode protection.

  • Code Optimization: Developed modular code in Embedded C++ on the Arduino IDE, optimizing loop delay timing to balance sampling rate and noise reduction.

  • System Validation: Used Serial Monitor debugging to visualize live sensor readings, validate switching logic, and fine-tune moisture threshold levels across different soil conditions.

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                                                      KEY LEARNINGS:

  • Embedded System Design and Control Logic:

    • Gained expertise in developing a complete microcontroller-based automation system using Arduino UNO.

    • Learned to implement decision-making algorithms based on analog sensor inputs and threshold-based event triggering.

  • Analog-to-Digital Data Processing:

    • Understood how the Arduino ADC subsystem converts soil sensor voltage signals into quantized values (0–1023).

    • Calibrated sensor readings for different soil compositions and moisture densities to enhance measurement accuracy.

  • Sensor Interfacing and Signal Conditioning:

    • Integrated capacitive moisture sensors with Arduino using voltage dividers and reference calibration techniques.

    • Learned how environmental factors (temperature, soil texture) influence sensor resistance and data stability.

  • Relay and Power Electronics Integration:

    • Designed a safe switching mechanism for motor activation using relay driver circuits, understanding isolation, hysteresis, and inrush current control.

    • Ensured hardware safety through appropriate selection of flyback diodes and pull-up resistors to prevent back-EMF damage.

  • Control System Optimization:

    • Tuned control parameters to balance responsiveness and stability using feedback loops and timing control logic.

    • Implemented debounce and delay mechanisms to eliminate false triggers due to fluctuating analog readings.

  • Debugging and Data Logging:

    • Utilized Serial communication (UART) for real-time data visualization and iterative testing.

    • Learned to debug hardware faults, such as sensor disconnections and floating analog inputs, using systematic electrical troubleshooting.

  • System Scalability and IoT Readiness:

    • Understood how the design could be extended to Wi-Fi or GSM modules (ESP8266, NodeMCU) for IoT-based monitoring and remote automation.

    • Designed the system with modular hardware and code architecture to support future integrations like mobile apps, dashboards, and environmental sensors.

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