Enhancing Agricultural Operations: Big Data Analytics Using Distributed and Parallel Computing

This comprehensive research investigates using distributed and parallel computing for big data analytics in agriculture to improve farming operations' sustainability, efficiency, and innovation. The paper emphasizes how big data analytics, cloud computing, and parallel distributed processing ca...

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Bibliographic Details
Published in:International Journal of Emerging Engineering and Technology Vol. 2; no. 2; pp. 1 - 7
Main Authors: Fatima, Syeda Alishba, Syeda Faiza Nasim, Saad Ahmed
Format: Journal Article
Language:English
Published: Swedish College of Egineering and Technology 30.12.2023
Subjects:
analytics
Big data
paralellization
ISSN:2958-3764, 2958-3764
Online Access:Get full text
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Summary:This comprehensive research investigates using distributed and parallel computing for big data analytics in agriculture to improve farming operations' sustainability, efficiency, and innovation. The paper emphasizes how big data analytics, cloud computing, and parallel distributed processing can revolutionize the agricultural industry. The research objectives include investigating the benefits and limitations of big data analytics in precision farming and crop monitoring, identifying the constraints of integrating big data analytics in agriculture and investigating the role of frameworks such as Hadoop and Spark in processing and analyzing agricultural data for informed decision-making and optimized farming operations. The methodology used in the paper is a literature review, which draws on various sources to provide insights into the topic matter. The findings indicate that big data analytics can considerably improve precision farming and crop monitoring; nevertheless, there are hurdles to incorporating big data analytics in agriculture, such as data privacy and security concerns. According to the study, frameworks such as Hadoop and Spark are crucial in processing and analyzing agricultural data for informed decision-making and better farming operations. Overall, this study offers useful insights into the possibilities of big data analytics and distributed and parallel computing in revolutionizing the agriculture industry.
ISSN:2958-3764
2958-3764
DOI:10.57041/v3jj9f69