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

Mandeep Kaur Saggi1, Sushma Jain1

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Big data analytics and machine learning offer powerful tools for sustainable agriculture, particularly in optimizing irrigation water management. This review explores intelligent learning approaches for data-driven decision support systems to enhance crop water efficiency.

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Area of Science:

  • Agricultural Science
  • Data Science
  • Environmental Science

Background:

  • Big data analytics and machine learning are increasingly vital in agriculture.
  • Effective irrigation water management is crucial for sustainable farming, influenced by climate, soil, and weather.
  • Accurate crop water requirement estimation necessitates robust modeling.

Purpose of the Study:

  • To review the application of big data-based decision support systems for sustainable irrigation water management.
  • To examine leveraging data, models, and analytics for next-generation agricultural water systems.
  • To highlight the need for integrating big data and ICT in irrigation management through analytical modeling.

Main Methods:

  • Literature review of big data analytics, machine learning, and intelligent learning approaches.
  • Analysis of decision support system frameworks for irrigation management.
  • Examination of crop water models and irrigation scheduling methods.

Main Results:

  • Big data analytics provides a key technology for analyzing voluminous agricultural data.
  • Intelligent learning approaches can enhance the accuracy of irrigation water requirement estimations.
  • Data-driven decision support systems are essential for optimizing water use in smart agriculture.

Conclusions:

  • The integration of big data technologies and ICT is critical for advancing irrigation water management.
  • Analytical modeling approaches are key to developing effective applications for smart agriculture.
  • Further research is needed to leverage intelligent learning for next-generation irrigation systems.