Smart irrigation systems using machine learning and control theory

Abstract

Intelligent irrigation systems have recently gained importance in terms of efficient cultivation of plants and the correct use of water on earth. Therefore, studies, such as plant growth modeling, irrigation modeling, and control continue, in this field. Plant growth modeling creates the infrastructure for the most accurate irrigation and fertilization activities in terms of crop yield. In addition, irrigation modeling and control is the efficient use of water resources to irrigate the entire plant system adequately. Machine learning (ML) methods are very suitable for modeling and prediction, and many studies have been done in the literature for plant growth modeling and irrigation. On the other hand, control theory methods ensure that the desired irrigation amount is made precisely. In addition, remote control approaches are an important step that facilitates irrigation systems. In this study, it is explained how ML and control methods are used in plant growth modeling and irrigation systems. In addition, current problems are discussed at the end then possible future implementation of the new approaches are explained at the end of the chapter. © 2022 Scrivener Publishing LLC.