Development of IOT-Based Indoor Plant Growth Optimization System/ Alwyn Kylle Q. Cabatuando. Kervy P. De Leon, Stephen Hiroshi M. Koizumi, and Jomarie Carl Pantin..-

Bachelor's Thesis

College of Industrial Technology..- Bachelor of Engineering Technology major in Computer Engineering Technology: Technological University of the Philippines, 2024.

Includes bibliographic references and index.

The integration of technology with traditional gardening practices has led to the
development of smart plant pots, offering an advanced approach to plant care. Traditional
methods of plant monitoring often rely on visual cues which can be subjective and lead to
under or over-care. The development of an IoT-based Indoor Plant Growth Optimization
System is designed to monitor the Plant’s health and environmental status. The
development of an IoT-based Indoor Plant Growth Optimization System is equipped with
various sensors and features. The core components include a microprocessor, temperature
sensor, soil moisture sensor, light sensor, humidity sensor, pH level sensor, light system,
and self-watering mechanism. The microprocessor serves as the brain of the device and a
Wi-Fi module that interacts with a mobile device that sends data to the user for analysis
and to have a result. A mobile application that interprets the data sent by the
microprocessor, shows the everyday record of the plant status that can be stored in the
database. The history of everyday data is shown in the mobile application for better
monitoring and transcript of records. Through the integration of the components and
capabilities, the IoT-based Indoor Plant Growth Optimization System empowers users to
effectively monitor and manage plant health, optimizing conditions, and reducing the risk
associated with improper care. A data-driven approach to plant care enables users to make
an informed decision based on objective information. It aims to highlight the potential
benefits of the device and its significant contribution to the environment.
Keywords: plant monitoring, sensors, microprocessor, mobile application, data-driven