Fuzzy irrigation model in protected crop based on expert knowledge

Alain Godo Alonso; Dennis Villavicencio Quintero; Emilio C abrera Hernández; Ivan Santana Ching

doi:10.5935/jetia.v11i52.1926

Autores

Alain Godo Alonso SORBA.ai, Jacksonville, FL, USA https://orcid.org/0000-0002-1501-0696 (não autenticado)
Dennis Villavicencio Quintero University "Marta Abreu" of Las Villas, Santa Clara, Cuba http://orcid.org/0000-0002-5433-6947 (não autenticado)
Emilio C abrera Hernández University "Marta Abreu" of Las Villas, Santa Clara, Cuba http://orcid.org/0000-0003-3142-7253 (não autenticado)
Ivan Santana Ching University "Marta Abreu" of Las Villas, Santa Clara, Cuba https://orcid.org/0000-0001-5089-520X (não autenticado)

DOI:

https://doi.org/10.5935/jetia.v11i52.1926

Resumo

Fuzzy logic is a subfield of Artificial Intelligence that allows human knowledge to be expressed naturally, through linguistic variables and values, and an inference process very similar to the one it uses daily. The present research uses expert criteria to design and evaluate a model based on a fuzzy system to predict the irrigation time of the protected crop of cucumber (Cucumis sativus L.). The variables temperature, soil moisture and lighting are used for the model construction, which is coupled to an existing IoT technology in the various crops company "Valle del Yabú", serving as a support system for decision making. The prototype is created and simulated in MATLAB, then transferred to a Raspberry Pi 4 Model B, using the Python programming language. Tests using a database collected during one crop cycle show a 10.07% reduction in water usage compared to the standard irrigation currently implemented by the company.