Canlas, Kian Therese M.; Cortez, Joana May T.; Delantar, Yvanna Lou C.  4  0   <br/><br/>
Mix and Match: AI-driven plant matching for indoor spaces with arduino-based air quality monitoring /        6 
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Canlas, Kian Therese M.; Cortez, Joana May T.; Delantar, Yvanna Lou C. 
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<br/><br/>   Undergraduate Thesis: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2024. 
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<br/><br/> ABSTRACT:  	The study Mix and Match: AI-Driven Plant Matching for Indoor Spaces with Arduino-Based Air Quality Monitoring seeks to improve indoor air quality using a new approach. Using artificial intelligence, the system recommends appropriate indoor plants based on user preferences and also has air quality data collected by an Arduino-based monitor. This combination of artificial intelligence and hardware technologies allows for more tailored plant selections, resulting in healthier indoor settings. The study tackles the growing concern about indoor air quality and proposes an innovative approach to improve well-being by seamlessly merging technology and nature.  	The incorporation of hardware technologies, such as Arduino-based air quality monitors, lends a real dimension to the system, providing users with actionable insights regarding the present of their interior environment. This real-time feedback method allows users to make informed decisions about plant selection and placement, resulting in a healthier and more harmonious indoor living environment.  	Beyond its obvious practical uses, the study addresses a major societal issue - the deterioration of indoor air quality - and proposes a comprehensive solution that perfectly merges technology and nature. By encouraging the widespread use of indoor plants as natural air purifiers, the study has the potential to greatly improve the well-being and quality of life for countless people in both home and business settings.  	In a nutshell Mix and Match: AI-Driven Plant Matching for Indoor Spaces with Arduinop-Based Air Quality Monitoring is a ground-breaking effort to address the complicated challenge of indoor air quality through an innovative combination of artificial intelligence and hardware technology. The study's approach to generating healthier, more sustainable indoor settings is transformational, merging powerful algorithm with real-time environmental data.       
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