A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems
Cultivating in greenhouses constitutes a fundamental tool for the development of high-quality crops with a high degree of profitability. Prediction and control models guarantee the correct management of environment variables, for which fuzzy inference systems have been successfully implemented. The...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Human-Computer Interaction |
| Online Access: | http://dx.doi.org/10.1155/2022/8483003 |
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| author | Sebastian-Camilo Vanegas-Ayala Julio Barón-Velandia Daniel-David Leal-Lara |
| author_facet | Sebastian-Camilo Vanegas-Ayala Julio Barón-Velandia Daniel-David Leal-Lara |
| author_sort | Sebastian-Camilo Vanegas-Ayala |
| collection | DOAJ |
| description | Cultivating in greenhouses constitutes a fundamental tool for the development of high-quality crops with a high degree of profitability. Prediction and control models guarantee the correct management of environment variables, for which fuzzy inference systems have been successfully implemented. The purpose of this review is determining the various relationships in fuzzy inference systems currently used for the modelling, prediction, and control of humidity in greenhouses and how they have changed over time to be able to develop more robust and easier to understand models. The methodology follows the PRISMA work guide. A total of 93 investigations in 4 academic databases were reviewed; their bibliometric aspects, which contribute to the objective of the investigation, were extracted and analysed. It was finally concluded that the development of models based in Mamdani fuzzy inference systems, integrated with optimization and fuzzy clustering techniques, and following strategies such as model-based predictive control guarantee high levels of precision and interpretability. |
| format | Article |
| id | doaj-art-d22af2d6c016402cb87c1afcefe3c9e6 |
| institution | Kabale University |
| issn | 1687-5907 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Human-Computer Interaction |
| spelling | doaj-art-d22af2d6c016402cb87c1afcefe3c9e62025-02-03T01:01:29ZengWileyAdvances in Human-Computer Interaction1687-59072022-01-01202210.1155/2022/8483003A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference SystemsSebastian-Camilo Vanegas-Ayala0Julio Barón-Velandia1Daniel-David Leal-Lara2Faculty of EngineeringFaculty of EngineeringFaculty of EngineeringCultivating in greenhouses constitutes a fundamental tool for the development of high-quality crops with a high degree of profitability. Prediction and control models guarantee the correct management of environment variables, for which fuzzy inference systems have been successfully implemented. The purpose of this review is determining the various relationships in fuzzy inference systems currently used for the modelling, prediction, and control of humidity in greenhouses and how they have changed over time to be able to develop more robust and easier to understand models. The methodology follows the PRISMA work guide. A total of 93 investigations in 4 academic databases were reviewed; their bibliometric aspects, which contribute to the objective of the investigation, were extracted and analysed. It was finally concluded that the development of models based in Mamdani fuzzy inference systems, integrated with optimization and fuzzy clustering techniques, and following strategies such as model-based predictive control guarantee high levels of precision and interpretability.http://dx.doi.org/10.1155/2022/8483003 |
| spellingShingle | Sebastian-Camilo Vanegas-Ayala Julio Barón-Velandia Daniel-David Leal-Lara A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems Advances in Human-Computer Interaction |
| title | A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems |
| title_full | A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems |
| title_fullStr | A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems |
| title_full_unstemmed | A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems |
| title_short | A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems |
| title_sort | systematic review of greenhouse humidity prediction and control models using fuzzy inference systems |
| url | http://dx.doi.org/10.1155/2022/8483003 |
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