A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches.

Autor: Saggi MK; Department of Computer Science, Thapar Institute of Engineering & Technology, Patiala, India., Jain S; Department of Computer Science, Thapar Institute of Engineering & Technology, Patiala, India.
Jazyk: angličtina
Zdroj: Archives of computational methods in engineering : state of the art reviews [Arch Comput Methods Eng] 2022; Vol. 29 (6), pp. 4455-4478. Date of Electronic Publication: 2022 May 09.
DOI: 10.1007/s11831-022-09746-3
Abstrakt: From last decade, Big data analytics and machine learning is a hotspot research area in the domain of agriculture. Agriculture analytics is a data intensive multidisciplinary problem. Big data analytics becomes a key technology to perform analysis of voluminous data. Irrigation water management is a challenging task for sustainable agriculture. It depends on various parameters related to climate, soil and weather conditions. For accurate estimation of requirement of water for a crop a strong modeling is required. This paper aims to review the application of big data based decision support system framework for sustainable water irrigation management using intelligent learning approaches. We examined how such developments can be leveraged to design and implement the next generation of data, models, analytics and decision support tools for agriculture irrigation water system. Moreover, water irrigation management need to rapidly adapt state-of-the-art using big data technologies and ICT information technologies with the focus of developing application based on analytical modeling approach. This study introduces the area of research, including a irrigation water management in smart agriculture, the crop water model requirement, and the methods of irrigation scheduling, decision support system, and research motivation.
Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest.
(© The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2022.)
Databáze: MEDLINE
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