Model predictive control for precision irrigation of a quinoa crop

• Autores: Oliden Semino, José Carlos, Beltrán Ccama, Iván, Faccini Santoro, Bruno
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/6538
• Enlace del recurso: https://hdl.handle.net/20.500.12867/6538; https://doi.org/10.1515/chem-2022-0264
• Nivel de acceso: acceso abierto
• Materia: Predictive modelling; Irrigation; Quinoa; Agriculture; https://purl.org/pe-repo/ocde/ford#4.01.01

Traditional High Andean agriculture is rainfed, and irrigation is commonly carried out in an open loop, that is, without measuring variables such as soil moisture content or plant development to define water consumption. This article presents model predictive control applied to irrigation systems under real conditions, whose purpose is the efficient use of water in rainfed crops with improved yield and crop productivity at minimum water consumption. The article presents a control strategy applying a model of predictive control that calculates the optimal amount of water for daily irrigation under real conditions. The most important attraction of the model is the prediction and future behavior of the controlled variables as a function of the changes in the manipulated variables. The objective is to improve the yield of the crop at minimum water consumption, for this, it will be necessary to use models that link with the Aquacrop software and allow it to be a source of data, and for the prediction of future values. The predictive controller is evaluated in the Quinoa crop (Chenopodium Quinoa Willdenow), and the performance is compared against existing traditional irrigation data in the literature. The results indicate that the predictive controller can achieve higher crop efficiency and reduce irrigation water supplies considerably.