Precision agriculture: Leveraging data science for sustainable farming

Adeleye Yusuff Adewuyi 1, Blessing Anyibama 2, Kayode Blessing Adebayo 3, Joseph Moses Kalinzi 4, Samson Ademola Adeniyi 5 and Ifeoluwa Wada 6, *

1 The Romain College of Business, University of Southern Indiana, Evansville, USA.
2 Department of Food and Nutritional Sciences, Tuskegee University, Alabama, USA.
3 Department of Agricultural and Biosystems Engineering, South Dakota State University, USA.
4 Department of Computer Science, Southern Illinois University Carbondale, Illinois, USA.
5 College of Computing and Informatics, Drexel University, PA. USA.
6 Information Technology, Emporia State University, Kansas, USA.
 
Review
International Journal of Science and Research Archive, 2024, 12(02), 1122–1129.
Article DOI: 10.30574/ijsra.2024.12.2.1371
Publication history: 
Received on 14 June 2024; revised on 22 July 2024; accepted on 25 July 2024
 
Abstract: 
Precision agriculture leverages data science and technology to optimize farming practices, enhance crop yields, and promote sustainability. This review examines how data-driven approaches are revolutionizing agriculture through improved decision-making, resource efficiency, and environmental stewardship. Key technologies enabling precision agriculture are discussed, including remote sensing, IoT sensors, AI/machine learning, and farm management information systems. The paper explores applications such as variable rate technology, yield mapping, and predictive analytics. Challenges related to data management, interoperability, and adoption barriers are analyzed. The review concludes that precision agriculture, powered by data science, offers significant potential to address food security and sustainability challenges, but requires continued research and interdisciplinary collaboration to realize its full benefits.
 
Keywords: 
Precision agriculture; Data science; Sustainable farming; IoT sensors; Artificial intelligence; Predictive analytics
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0