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

Clicks: 1
ID: 286034
2021
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #3,169 of 3,757 articles by views in Malay Journal

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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.
Reference Key
persistent_1760657375_68f17fdf693ec Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lauguico, Sandy C.
Journal Malay Journal
Year 2021
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdm_ece/5
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.