Computational intelligence-based automation and control for adaptive management system (AMS) of a smart aquaponics

As the population is expected to increase to 9.8 billion in 2050, according to the United Nations, there is an increasing demand for food and space due to the continuous increase of population density. This causes rural areas which were originally the base for agricultural development to be transfor...

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Main Author: Lauguico, Sandy C.
Format: text
Language:English
Published: Animo Repository 2021
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Online Access:https://animorepository.dlsu.edu.ph/etdm_ece/5
https://animorepository.dlsu.edu.ph/context/etdm_ece/article/1004/viewcontent/Lauguico2.pdf
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Institution: De La Salle University
Language: English
id oai:animorepository.dlsu.edu.ph:etdm_ece-1004
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spelling oai:animorepository.dlsu.edu.ph:etdm_ece-10042022-10-10T00:59:08Z Computational intelligence-based automation and control for adaptive management system (AMS) of a smart aquaponics Lauguico, Sandy C. As the population is expected to increase to 9.8 billion in 2050, according to the United Nations, there is an increasing demand for food and space due to the continuous increase of population density. This causes rural areas which were originally the base for agricultural development to be transformed into urban areas. Urbanization now causes food insecurity. Addressing the issues on urbanization, urban farming has now become a feasible solution to meet the growing demand of food and space. Providing a Close Environment Agriculture (CEA) is both a challenge and a solution in facing development and establishment of urban farms. An Adaptive Management System (AMS) is necessary to operate such systems to provide an artificial environment suitable to grow and produce cultivars effectively resulting in sustainable efficiency. This research proposes the development of a computational intelligence-based automation and control system utilizing machine and deep learning models for evaluating product quality. Quality assessments are then used for adjusting the environmental parameters with respect to the cultivars’ needs. The system is to be composed of sensors for data acquisition, as well as actuators for model-dictated responses to stimuli. Data logging will be done wirelessly through a router which would collect and monitor data through a cloud-based dashboard. The model that will undergo training from the data acquired will undergo statistical comparative analysis and least computational cost analysis to improve the performance. System performance will also be evaluated with the monitoring of the status and conditions of the sensors and actuators. 2021-03-01T08:00:00Z text application/pdf https://animorepository.dlsu.edu.ph/etdm_ece/5 https://animorepository.dlsu.edu.ph/context/etdm_ece/article/1004/viewcontent/Lauguico2.pdf Electronics And Communications Engineering Master's Theses English Animo Repository Aquaponics—Automatic control Computational intelligence Computer vision Electrical and Computer Engineering Electrical and Electronics Systems and Communications
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Aquaponics—Automatic control
Computational intelligence
Computer vision
Electrical and Computer Engineering
Electrical and Electronics
Systems and Communications
spellingShingle Aquaponics—Automatic control
Computational intelligence
Computer vision
Electrical and Computer Engineering
Electrical and Electronics
Systems and Communications
Lauguico, Sandy C.
Computational intelligence-based automation and control for adaptive management system (AMS) of a smart aquaponics
description As the population is expected to increase to 9.8 billion in 2050, according to the United Nations, there is an increasing demand for food and space due to the continuous increase of population density. This causes rural areas which were originally the base for agricultural development to be transformed into urban areas. Urbanization now causes food insecurity. Addressing the issues on urbanization, urban farming has now become a feasible solution to meet the growing demand of food and space. Providing a Close Environment Agriculture (CEA) is both a challenge and a solution in facing development and establishment of urban farms. An Adaptive Management System (AMS) is necessary to operate such systems to provide an artificial environment suitable to grow and produce cultivars effectively resulting in sustainable efficiency. This research proposes the development of a computational intelligence-based automation and control system utilizing machine and deep learning models for evaluating product quality. Quality assessments are then used for adjusting the environmental parameters with respect to the cultivars’ needs. The system is to be composed of sensors for data acquisition, as well as actuators for model-dictated responses to stimuli. Data logging will be done wirelessly through a router which would collect and monitor data through a cloud-based dashboard. The model that will undergo training from the data acquired will undergo statistical comparative analysis and least computational cost analysis to improve the performance. System performance will also be evaluated with the monitoring of the status and conditions of the sensors and actuators.
format text
author Lauguico, Sandy C.
author_facet Lauguico, Sandy C.
author_sort Lauguico, Sandy C.
title Computational intelligence-based automation and control for adaptive management system (AMS) of a smart aquaponics
title_short Computational intelligence-based automation and control for adaptive management system (AMS) of a smart aquaponics
title_full Computational intelligence-based automation and control for adaptive management system (AMS) of a smart aquaponics
title_fullStr Computational intelligence-based automation and control for adaptive management system (AMS) of a smart aquaponics
title_full_unstemmed Computational intelligence-based automation and control for adaptive management system (AMS) of a smart aquaponics
title_sort computational intelligence-based automation and control for adaptive management system (ams) of a smart aquaponics
publisher Animo Repository
publishDate 2021
url https://animorepository.dlsu.edu.ph/etdm_ece/5
https://animorepository.dlsu.edu.ph/context/etdm_ece/article/1004/viewcontent/Lauguico2.pdf
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