Computerized Irrigation Scheduling

Document Type

Conference Proceeding

Source of Publication

2023 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA)

Publication Date

12-7-2023

Abstract

Wasteful irrigation systems are significant contributors to water scarcity on the globe. Irrigation Scheduling based on Machine Learning (ML) algorithms is considered essential in helping reduce these wastes significantly. We conducted in this study a systematic mapping of ML-based Irrigation scheduling to identify how researchers approached Irrigation Scheduling and which ML models have been used in this area. It builds a comprehensive overview of what has been investigated on irrigation scheduling and discusses the open issues to be addressed in the future.

ISBN

979-8-3503-1943-9

Publisher

IEEE

Volume

00

First Page

1

Last Page

8

Disciplines

Engineering

Keywords

Irrigation Scheduling, Machine Learning, ML-based, Water scarcity, Systematic mapping

Indexed in Scopus

no

Open Access

no

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