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Smart Agriculture Resource Allocation and Cost Optimization Using ML in Cloud Computing Environment
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Author(s): Pancham Singh (Ajay Kumar Garg Engineering College, Ghaziabad, India), Mrignainy Kansal (Ajay Kumar Garg Engineering College, Ghaziabad, India), Mili Srivastava (Ajay Kumar Garg Engineering College, Ghaziabad, India)and Muskan Gupta (Ajay Kumar Garg Engineering College, Ghaziabad, India)
Copyright: 2023
Pages: 12
Source title:
Convergence of Cloud Computing, AI, and Agricultural Science
Source Author(s)/Editor(s): Avinash Kumar Sharma (ABES Institute of Technology, India), Nitin Chanderwal (University of Cincinnati, USA)and Rijwan Khan (Galgotias University, India)
DOI: 10.4018/979-8-3693-0200-2.ch008
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Abstract
In this research, resource allocation in machine learning is used to analyze how cloud computing is being applied in smart agriculture. This chapter goes over the advantages of cloud computing for farming and how machine learning can enhance resource allocation for higher agricultural yields and less negative environmental impact. The chapter also examines implementation difficulties for cloud-based agricultural solutions and speculates on potential fixes. Insights for researchers and practitioners in the area are provided by the research, which demonstrates the potential for merging cloud computing and machine learning in smart agriculture to increase productivity and sustainability. The research also assessed the efficacy of the ML-based techniques using a variety of performance indicators, including reaction time and throughput. The management of cloud workloads has shown considerable promise when using machine learning-based methods. This chapter offers a thorough overview of current developments in ML-based cloud workload management and identifies areas for further research.
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