A Review of the Application of Data Science and Machine Learning in Agricultural Water Management
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 pla...
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Ferdowsi University of Mashhad
2024-08-01
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| Series: | آب و توسعه پایدار |
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| Online Access: | https://jwsd.um.ac.ir/article_45619_e69d7f9d4a5aa21207c846b31bb68872.pdf |
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| author | Reza Delbaz Hamed Ebrahimian |
| author_facet | Reza Delbaz Hamed Ebrahimian |
| author_sort | Reza Delbaz |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fb0f5b804d324a50a78dd6bfb78ca0c4 |
| institution | OA Journals |
| issn | 2423-5474 2717-3321 |
| language | fas |
| publishDate | 2024-08-01 |
| publisher | Ferdowsi University of Mashhad |
| record_format | Article |
| series | آب و توسعه پایدار |
| spelling | doaj-art-fb0f5b804d324a50a78dd6bfb78ca0c42025-08-20T02:39:04ZfasFerdowsi University of Mashhadآب و توسعه پایدار2423-54742717-33212024-08-01112395610.22067/jwsd.v11i2.2402.131045619A Review of the Application of Data Science and Machine Learning in Agricultural Water ManagementReza Delbaz0Hamed Ebrahimian1Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.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.https://jwsd.um.ac.ir/article_45619_e69d7f9d4a5aa21207c846b31bb68872.pdfirrigationdata miningremote sensingwater managementartificial intelligence |
| spellingShingle | Reza Delbaz Hamed Ebrahimian A Review of the Application of Data Science and Machine Learning in Agricultural Water Management آب و توسعه پایدار irrigation data mining remote sensing water management artificial intelligence |
| title | A Review of the Application of Data Science and Machine Learning in Agricultural Water Management |
| title_full | A Review of the Application of Data Science and Machine Learning in Agricultural Water Management |
| title_fullStr | A Review of the Application of Data Science and Machine Learning in Agricultural Water Management |
| title_full_unstemmed | A Review of the Application of Data Science and Machine Learning in Agricultural Water Management |
| title_short | A Review of the Application of Data Science and Machine Learning in Agricultural Water Management |
| title_sort | review of the application of data science and machine learning in agricultural water management |
| topic | irrigation data mining remote sensing water management artificial intelligence |
| url | https://jwsd.um.ac.ir/article_45619_e69d7f9d4a5aa21207c846b31bb68872.pdf |
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