Greenhouse Humidity Prediction and Control Models by Using Fuzzy Inference Systems

Abstract

Greenhouse cultivation is an important technique for producing high-quality crops with a high profit margin. Fuzzy inference systems have been effectively utilized in prediction and control models to enable good management of environmental factors. The aim of this review is to determine the different relationships in the fuzzy inference systems currently used for modelling, prediction and humidity control in greenhouses and their evolution over time in order to develop more robust and reliable models easy to understand. The main objective is to identify the different relationships within fuzzy inference systems their configurations and models through optimization algorithms currently used for prediction, control and humidity modelling in greenhouses. The methodology follows the PRISMA working guide. A total of 93 surveys were reviewed in 4 academic databases; its bibliometric aspects have been extracted and analysed, which contributes to the objective of the survey. Finally, it was determined that using Mamdani's fuzzy inference systems in conjunction with optimization and fuzzy clustering approaches, as well as strategies like model-based predictive control, ensures high levels of accuracy and interpretability.