Smart Operation of Climatic Systems in a Greenhouse
The purpose of our work is to leverage the use of artificial intelligence for the emergence of smart greenhouses. Greenhouse agriculture is a sustainable solution for food crises and therefore data-based decision-support mechanisms are needed to optimally use them. Our study anticipates how the comb...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/12/10/1729 |
| _version_ | 1797476209175560192 |
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| author | Aurora González-Vidal José Mendoza-Bernal Alfonso P. Ramallo Miguel Ángel Zamora Vicente Martínez Antonio F. Skarmeta |
| author_facet | Aurora González-Vidal José Mendoza-Bernal Alfonso P. Ramallo Miguel Ángel Zamora Vicente Martínez Antonio F. Skarmeta |
| author_sort | Aurora González-Vidal |
| collection | DOAJ |
| description | The purpose of our work is to leverage the use of artificial intelligence for the emergence of smart greenhouses. Greenhouse agriculture is a sustainable solution for food crises and therefore data-based decision-support mechanisms are needed to optimally use them. Our study anticipates how the combination of climatic systems will affect the temperature and humidity of the greenhouse. More specifically, our methodology anticipates if a set-point will be reached in a given time by a combination of climatic systems and estimates the humidity at that time. We performed exhaustive data analytics processing that includes the interpolation of missing values and data augmentation, and tested several classification and regression algorithms. Our method can predict with a 90% accuracy if, under current conditions, a combination of climatic systems will reach a fixed temperature set-point, and it is also able to estimate the humidity with a 2.83% CVRMSE. We integrated our methodology on a three-layer holistic IoT platform that is able to collect, fuse and analyze real data in a seamless way. |
| first_indexed | 2024-03-09T20:55:43Z |
| format | Article |
| id | doaj.art-a9042e6ff5744c189b53ea0b7881ec8c |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T20:55:43Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-a9042e6ff5744c189b53ea0b7881ec8c2023-11-23T22:23:12ZengMDPI AGAgriculture2077-04722022-10-011210172910.3390/agriculture12101729Smart Operation of Climatic Systems in a GreenhouseAurora González-Vidal0José Mendoza-Bernal1Alfonso P. Ramallo2Miguel Ángel Zamora3Vicente Martínez4Antonio F. Skarmeta5Department of Information and Communication Engineering, University of Murcia, 30100 Murcia, SpainDepartment of Information and Communication Engineering, University of Murcia, 30100 Murcia, SpainDepartment of Information and Communication Engineering, University of Murcia, 30100 Murcia, SpainDepartment of Information and Communication Engineering, University of Murcia, 30100 Murcia, SpainDepartment of Vegetal Nutrition, Centro de Edafología y Biología Aplicada del Segura del Consejo Superior de Investigaciones Científicas (CEBAS-CSIC), 30100 Murcia, SpainDepartment of Information and Communication Engineering, University of Murcia, 30100 Murcia, SpainThe purpose of our work is to leverage the use of artificial intelligence for the emergence of smart greenhouses. Greenhouse agriculture is a sustainable solution for food crises and therefore data-based decision-support mechanisms are needed to optimally use them. Our study anticipates how the combination of climatic systems will affect the temperature and humidity of the greenhouse. More specifically, our methodology anticipates if a set-point will be reached in a given time by a combination of climatic systems and estimates the humidity at that time. We performed exhaustive data analytics processing that includes the interpolation of missing values and data augmentation, and tested several classification and regression algorithms. Our method can predict with a 90% accuracy if, under current conditions, a combination of climatic systems will reach a fixed temperature set-point, and it is also able to estimate the humidity with a 2.83% CVRMSE. We integrated our methodology on a three-layer holistic IoT platform that is able to collect, fuse and analyze real data in a seamless way.https://www.mdpi.com/2077-0472/12/10/1729smart agriculturegreenhouse technologiesartificial intelligence |
| spellingShingle | Aurora González-Vidal José Mendoza-Bernal Alfonso P. Ramallo Miguel Ángel Zamora Vicente Martínez Antonio F. Skarmeta Smart Operation of Climatic Systems in a Greenhouse Agriculture smart agriculture greenhouse technologies artificial intelligence |
| title | Smart Operation of Climatic Systems in a Greenhouse |
| title_full | Smart Operation of Climatic Systems in a Greenhouse |
| title_fullStr | Smart Operation of Climatic Systems in a Greenhouse |
| title_full_unstemmed | Smart Operation of Climatic Systems in a Greenhouse |
| title_short | Smart Operation of Climatic Systems in a Greenhouse |
| title_sort | smart operation of climatic systems in a greenhouse |
| topic | smart agriculture greenhouse technologies artificial intelligence |
| url | https://www.mdpi.com/2077-0472/12/10/1729 |
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