Deep Learning Using Genetic Algorithm And Evolutionary Algorithm For Automation Of Irrigation Systems

Authors

  • Ms Shanthi D L, Anitha Christy Angelin.P, Preethi S, Dasari Anantha Reddy, Dr. Surindar Gopalrao Wawale, Abdus Subhahan D

Abstract

Its employment of evolving methods as a technique for the successful management of irrigated fluid supplies throughout the globe is discussed in this study. This research entails a thorough examination of current studies that have used various forms of evolving methods to maximize restricted fluid supplies in quasi locations, having a focus on agricultural fluid control. The behaviors and outcomes of such approaches were explored in detail for various project kinds. Its effectiveness using various approaches throughout various iterations cycles too is examined, as well as problems that have to get solved. Agricultural liquid allotment or timing, irrigated management having a specific emphasis upon agricultural design or patterns, reservoirs management, or irrigated fluid transportation system are all included in the research. Due to its limited yearly rains in dry and quasi areas, it is critical that use accessible freshwater supplies for agriculture uses through irrigation to ensure product stability. The findings of this research may aid irrigated industry participants in determining the appropriate evolving method for specific optimization issues

Published

2021-10-01

How to Cite

Ms Shanthi D L, Anitha Christy Angelin.P, Preethi S, Dasari Anantha Reddy, Dr. Surindar Gopalrao Wawale, Abdus Subhahan D. (2021). Deep Learning Using Genetic Algorithm And Evolutionary Algorithm For Automation Of Irrigation Systems. Drugs and Cell Therapies in Hematology, 10(1), 2946–2954. Retrieved from http://dcth.org/index.php/journal/article/view/609

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Section

Articles