A Review of the Application of Data Science and Machine Learning in Agricultural Water Management

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Delbaz Reza; Ebrahimian Hamed

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Journal Paper

Paper Information

Journal: JOURNAL OF WATER AND SUSTAINABLE DEVELOPMENT Year:2024 | Volume:11 | Issue:2 Page(s): 39-56

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Information Journal Paper

Title

A Review of the Application of Data Science and Machine Learning in Agricultural Water Management

Author(s)

Delbaz Reza | Ebrahimian Hamed | Issue Writer Certificate

Keywords

Irrigation

Data mining

Remote sensing

Water management

Artificial Intelligence

Abstract

New technologies and innovations can improve Water management in agriculture. Data science and machine learning are emerging technologies. Data science is a growing field in the world of technology that helps analyze, extract information, and understand patterns and relationships in big data. It plays a pivotal role in a wide range of industries, including agriculture and environmental science. One field in which data science has a significant impact is water science and engineering. The aim of this research is to provide a comprehensive definition of data science and review existing studies in this field. According to the results, 10% of the studies conducted in the field of machine learning in agriculture are related to Water management. Furthermore, among all studies conducted in this field from 2018 to 2020, Iran accounted for 5.62% of the total. This field of research has primarily focused on determining crop evapotranspiration, predicting yield, and assessing water quality. However, given the novelty of this technology, there are still gaps in studies in this field, which is expected to be attracted by researchers in the future. On the other hand, like other emerging technologies, there are challenges in the implementation and execution of data science that require collaborative efforts among policymakers, researchers, and farmers to address. To resolve these challenges, it is necessary for these stakeholders to propose solutions that can optimally leverage the benefits of data science while simultaneously addressing the existing challenges and problems.

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