Abstract

Automatic irrigation scheduling systems are highly demanded in the agricultural sector due to their ability to both save water and manage deficit irrigation strategies. Elaborating a functional and efficient automatic irrigation system is a very complex task due to the high number of factors that the technician considers when managing irrigation in an optimal way. Automatic learning systems propose an alternative to traditional irrigation management by means of the automatic elaboration of predictions based on the learning of an agronomist (DSS). The aim of this paper is the study of several learning techniques in order to determine the goodness and error relative to expert decision. Nine orchards were tested during 2018 using linear regression (LR), random forest regression (RFR), and support vector regression (SVR) methods as engines of the irrigation decision support system (IDSS) proposed. The results obtained by the learning methods in three of these orchards have been compared with the decisions made by the agronomist over an entire year. The prediction model errors determined the best fitting regression model. The results obtained lead to the conclusion that these methods are valid engines to develop automatic irrigation scheduling systems.

Highlights

  • Water is a limiting factor in agricultural production

  • This paper presents the development of an irrigation decision support system (IDSS) for irrigation management optimization in citrus trees

  • We show the results obtained after the training and testing stages of each regression method, and we provide different measures to compare their performances

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Summary

Introduction

Water is a limiting factor in agricultural production. This fact is intensified in regions where water is scarce. In these regions, the importance of properly managing irrigation is a fundamental factor for sustainable production. There are agricultural techniques that have made it possible to optimize irrigation management, from the use of drip irrigation systems to regulated deficit irrigation strategies able to maintain yields with lower irrigation volumes [1,2]. Information and communication technologies (ICT) have contributed to the sustainable management of water in agriculture. The deployment of wireless sensor networks in crops using Internet

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