Journal
WATER
Volume 12, Issue 2, Pages -Publisher
MDPI
DOI: 10.3390/w12020548
Keywords
decision support systems; automatic irrigation scheduling; water optimization; machine learning
Categories
Funding
- Spanish Economy and Competitiveness Ministry (MINECO) [AGL2016-77282-C3-3-R]
- European Agricultural Funds for Rural Development [AGL2016-77282-C3-3-R]
- FundaciOn Seneca, Agencia de Ciencia y Tecnologia of the Region of Murcia under the Excelence Group Program [19895/GERM/15]
- Universidad Politecnica de Cartagena [5246/18 MAE]
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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.
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