A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems

Abstract

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.

Employee Productivity Assessment Using Fuzzy Inference System

“…Fuzzy logic modeling is particularly useful in complex and highly ambiguous situations, providing a framework for modeling complex nonlinear relationships. It offers several advantages over traditional mathematical modeling, including a transparent reasoning mechanism, the incorporation of linguistic data from human experts, the integration of numerical and linguistic information, and the ability to evaluate complex nonlinear functions with simple models [24]. In designing fuzzy sets, there are various approaches to interpreting and analyzing subjective data, such as using fuzzy rating scalebased questionnaires.…”

Artificial House for Swiftlets (COLLOCALIA FUCIPHAGA) Based on MAMDANI FIS (Fuzzy Inference System)

“…This method can create an ideal climate for precise plant growth with effective telemonitoring. The implementation of the Mamdani-type fuzzy inference system, optimized using a hybrid method combining genetic algorithms and interior point methods, allows for predicting relative humidity in a greenhouse with high interpretability and precision, achieving an effectiveness percentage of 90.97% and a mean square error (MSE) of 8.2e-3 [9].…”

Predictive Model of Humidity in Greenhouses through Fuzzy Inference Systems Applying Optimization Methods

“…However, similarly they pay little attention to model simplicity and interpretability, which is considered the main objective of fuzzy inference systems [6]. Terefore, it is necessary to fnd solutions that maintain both the system accuracy and its interpretability, guaranteeing a better performance as well as greater understanding of the modeled system [28].…”

“…A moisture prediction model with high interpretability is presented based on a complete analysis of the variables that interact in a greenhouse environment, guaranteeing high accuracy values with the optimization of each model, a strategy that is not currently exploited [28]. Te content was divided into the following sections: materials and methods, results, discussion, and conclusions.…”