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A genetic programming-based optimal sensor placement for greenhouse monitoring and control
Optimal sensor location methods are crucial to realize a sensor profile that achieves pre-defined performance criteria as well as minimum cost. In recent times, indoor cultivation systems have leveraged on optimal sensor location schemes for effective monitoring at minimum cost. Although the goal of...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288141/ https://www.ncbi.nlm.nih.gov/pubmed/37360731 http://dx.doi.org/10.3389/fpls.2023.1152036 |
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author | Ajani, Oladayo S. Aboyeji, Esther Mallipeddi, Rammohan Dooyum Uyeh, Daniel Ha, Yushin Park, Tusan |
author_facet | Ajani, Oladayo S. Aboyeji, Esther Mallipeddi, Rammohan Dooyum Uyeh, Daniel Ha, Yushin Park, Tusan |
author_sort | Ajani, Oladayo S. |
collection | PubMed |
description | Optimal sensor location methods are crucial to realize a sensor profile that achieves pre-defined performance criteria as well as minimum cost. In recent times, indoor cultivation systems have leveraged on optimal sensor location schemes for effective monitoring at minimum cost. Although the goal of monitoring in indoor cultivation system is to facilitate efficient control, most of the previously proposed methods are ill-posed as they do not approach optimal sensor location from a control perspective. Therefore in this work, a genetic programming-based optimal sensor placement for greenhouse monitoring and control is presented from a control perspective. Starting with a reference micro-climate condition (temperature and relative humidity) obtained by aggregating measurements from 56 dual sensors distributed within a greenhouse, we show that genetic programming can be used to select a minimum number of sensor locations as well as a symbolic representation of how to aggregate them to efficiently estimate the reference measurements from the 56 sensors. The results presented in terms of Pearson’s correlation coefficient (r) and three error-related metrics demonstrate that the proposed model achieves an average r of 0.999 for both temperature and humidity and an average RMSE value of 0.0822 and 0.2534 for temperate and relative humidity respectively. Conclusively, the resulting models make use of only eight (8) sensors, indicating that only eight (8) are required to facilitate the efficient monitoring and control of the greenhouse facility. |
format | Online Article Text |
id | pubmed-10288141 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102881412023-06-24 A genetic programming-based optimal sensor placement for greenhouse monitoring and control Ajani, Oladayo S. Aboyeji, Esther Mallipeddi, Rammohan Dooyum Uyeh, Daniel Ha, Yushin Park, Tusan Front Plant Sci Plant Science Optimal sensor location methods are crucial to realize a sensor profile that achieves pre-defined performance criteria as well as minimum cost. In recent times, indoor cultivation systems have leveraged on optimal sensor location schemes for effective monitoring at minimum cost. Although the goal of monitoring in indoor cultivation system is to facilitate efficient control, most of the previously proposed methods are ill-posed as they do not approach optimal sensor location from a control perspective. Therefore in this work, a genetic programming-based optimal sensor placement for greenhouse monitoring and control is presented from a control perspective. Starting with a reference micro-climate condition (temperature and relative humidity) obtained by aggregating measurements from 56 dual sensors distributed within a greenhouse, we show that genetic programming can be used to select a minimum number of sensor locations as well as a symbolic representation of how to aggregate them to efficiently estimate the reference measurements from the 56 sensors. The results presented in terms of Pearson’s correlation coefficient (r) and three error-related metrics demonstrate that the proposed model achieves an average r of 0.999 for both temperature and humidity and an average RMSE value of 0.0822 and 0.2534 for temperate and relative humidity respectively. Conclusively, the resulting models make use of only eight (8) sensors, indicating that only eight (8) are required to facilitate the efficient monitoring and control of the greenhouse facility. Frontiers Media S.A. 2023-06-09 /pmc/articles/PMC10288141/ /pubmed/37360731 http://dx.doi.org/10.3389/fpls.2023.1152036 Text en Copyright © 2023 Ajani, Aboyeji, Mallipeddi, Dooyum Uyeh, Ha and Park https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Ajani, Oladayo S. Aboyeji, Esther Mallipeddi, Rammohan Dooyum Uyeh, Daniel Ha, Yushin Park, Tusan A genetic programming-based optimal sensor placement for greenhouse monitoring and control |
title | A genetic programming-based optimal sensor placement for greenhouse monitoring and control |
title_full | A genetic programming-based optimal sensor placement for greenhouse monitoring and control |
title_fullStr | A genetic programming-based optimal sensor placement for greenhouse monitoring and control |
title_full_unstemmed | A genetic programming-based optimal sensor placement for greenhouse monitoring and control |
title_short | A genetic programming-based optimal sensor placement for greenhouse monitoring and control |
title_sort | genetic programming-based optimal sensor placement for greenhouse monitoring and control |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288141/ https://www.ncbi.nlm.nih.gov/pubmed/37360731 http://dx.doi.org/10.3389/fpls.2023.1152036 |
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