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Arduino-based aeroponics system for selected edible flowering plant with growth prediction model / Bartolome, Jann Reih C.; Dalumpines, Owen T.; Mercado, Angelo C.; Olivares, Mel John U. 6

By: Bartolome, Jann Reih C.; Dalumpines, Owen T.; Mercado, Angelo C.; Olivares, Mel John U. 4 0 16, [, ] | [, ] |

Contributor(s): 5 6 [] |

Language: Unknown language code Summary language: Unknown language code Original language: Unknown language code Series: ; January 2024.46Edition: Description: viii, 125 ppContent type: text Media type: unmediated Carrier type: volumeISBN: ISSN: 2Other title: 6 []Uniform titles: | | Related works: 1 40 6 []Subject(s): -- 2 -- 0 -- -- | -- 2 -- 0 -- 6 -- | 2 0 -- | -- -- 20 -- | | -- -- -- -- 20 -- | -- -- -- 20 -- --Genre/Form: -- 2 -- Additional physical formats: DDC classification: | LOC classification: | | 2Other classification:

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Action note: In: Summary: ABSTRACT: Agriculture has witnessed a shift towards sustainable and precision farming techniques, and aeroponics stands as an efficient method for plant growth. This study focuses on leveraging technology to create a system that optimizes cultivation and provides growth prediction , addressing the demand for smart and sustainable agricultural practices. This research seeks to answer the effectiveness of Arduino-based aeroponics in fostering plant growth, particularly in edible flowering plant. The integration of technology aims to enhance cultivation, automate processes, and offer growth prediction models. While previous research has highlighted the advantages of aeroponics, integrating predictive models into Arduino-based systems represents a novel approach for precision farming. The research addresses the need for sustainable agricultural practices and efficient food production. By exploring uncharted territory, it fills gaps in existing research, aiming to revolutionize plant cultivation with innovative methods. The study adopts an integrated approach combining hardware and software development, involving the construction of an Arduino-based aeroponics system, sensor integration, and predictive model creation. Specifically, a simple linear regression algorithm model is employed to enhance growth predictions. The findings reveal the successful development and implementation of the system. The integration of growth prediction models, including the simple linear regression model, exhibits promising results in enhancing plant care and yield. This research's implications extend to precision agriculture and sustainable food production. It introduces an innovative approach to plant cultivation, emphasizing automation, predictability, and resource efficiency. The findings suggest that this groundbreaking system, incorporating a simple linear regression algorithm model, could play a pivotal role in global food security challenges. Other editions:

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Item type	Current location	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	PLM	PLM Filipiniana Section	Filipiniana-Thesis	T58.5 .B37 2024 (Browse shelf)	Available		FT7812

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Undergraduate Thesis: (bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2024. 56

ABSTRACT: Agriculture has witnessed a shift towards sustainable and precision farming techniques, and aeroponics stands as an efficient method for plant growth. This study focuses on leveraging technology to create a system that optimizes cultivation and provides growth prediction , addressing the demand for smart and sustainable agricultural practices. This research seeks to answer the effectiveness of Arduino-based aeroponics in fostering plant growth, particularly in edible flowering plant. The integration of technology aims to enhance cultivation, automate processes, and offer growth prediction models. While previous research has highlighted the advantages of aeroponics, integrating predictive models into Arduino-based systems represents a novel approach for precision farming. The research addresses the need for sustainable agricultural practices and efficient food production. By exploring uncharted territory, it fills gaps in existing research, aiming to revolutionize plant cultivation with innovative methods. The study adopts an integrated approach combining hardware and software development, involving the construction of an Arduino-based aeroponics system, sensor integration, and predictive model creation. Specifically, a simple linear regression algorithm model is employed to enhance growth predictions. The findings reveal the successful development and implementation of the system. The integration of growth prediction models, including the simple linear regression model, exhibits promising results in enhancing plant care and yield. This research's implications extend to precision agriculture and sustainable food production. It introduces an innovative approach to plant cultivation, emphasizing automation, predictability, and resource efficiency. The findings suggest that this groundbreaking system, incorporating a simple linear regression algorithm model, could play a pivotal role in global food security challenges.

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