Greenhouse horticulture is a popular method for growing high-value crops with a large profit margin. To enable excellent management of environmental elements, fuzzy inference systems have been successfully used in prediction and control models. The goal of this research is to discover the many relationships in fuzzy inference systems now used for greenhouse modelling, prediction, and humidity management, as well as their change through time, in order to design more robust and understandable models. The major goal is to apply optimization techniques to find distinct linkages inside fuzzy inference systems, their configurations, and models, which are currently used for greenhouse forecast, control, and humidity modelling. The procedure is based on the PRISMA working guide. Four academic databases were combed through for a total of 93 questionnaires. Its bibliometric features have been retrieved and analysed, which helps the survey achieve its goal. Finally, it was discovered that combining Mamdani's fuzzy inference systems with optimization and fuzzy clustering methodologies, as well as tactics like model-based predictive control, assures great accuracy and interpretability.
Author(S) Details
Sebastian-Camilo Vanegas-Ayala
Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021-110231588, Colombia.
Julio Barón-Velandia
Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021-110231588, Colombia.
Daniel-David Leal-Lara
Faculty of Engineering, Universidad Distrital Francisco José de Caldas, Bogotá D.C. 11021-110231588, Colombia.
View Book:- https://stm.bookpi.org/RDST-V6/article/view/7012
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