Multicloud-Powered Agriculture: Enhancing Precision Farming Through IoT and Data Analytics

Authors

  • Abhishek Kumar Sinha St. Cloud State University, MN, USA Author

DOI:

https://doi.org/10.32628/CSEIT251112130

Keywords:

Multi-cloud Agriculture, Precision Farming, IoT Sensors, Deep Learning, Agricultural Automation

Abstract

Multi-cloud architectures are revolutionizing modern agriculture through enhanced precision farming capabilities and optimized resource utilization. These systems integrate Internet of Things (IoT) devices, advanced analytics, and machine learning technologies to transform traditional farming practices. The architecture encompasses comprehensive data collection from soil sensors, weather stations, drone imagery, and agricultural machinery, processed through distributed computing platforms. Deep learning models enable accurate crop yield predictions, early disease detection, and resource optimization. Data confidentiality and operational efficiency are maintained through the use of advanced security frameworks and regulatory compliance methods. Through automated decision-making and real-time monitoring, this technology integration shows notable gains in crop yields, resource conservation, and overall farming productivity.

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References

Nikita D. Shingarwade et al., "Performance Evaluation of Cloud Based Farm Automation," 2018, Available: https://ieeexplore.ieee.org/document/8463774

Bhargavi Renta Chintala, "Big Data Challenges and Opportunities in Agriculture," 2020. Available: https://www.researchgate.net/publication/338321539_Big_Data_Challenges_and_Opportunities_in_Agriculture

Heri Andrianto et al., "Performance evaluation of IoT-based service system for monitoring nutritional deficiencies in plants," 2023. Available: https://www.sciencedirect.com/science/article/pii/S2214317321000792

Jose A. Brenes et al., "Scalable Technological Architecture Empowers Small-Scale Smart Farming Solutions," 2024. Available: https://dl.acm.org/doi/10.1145/3653327

Jide Kehinde Adeniyi et al., "A Comparative Analysis of the Performance of Deep Learning Techniques in Precision Farming Using Soil and Climate Factors," 2024. Available: https://www.sciencedirect.com/science/article/pii/S1877050924009451

Mohamed El Mehdi El Aissi et al., "A Scalable Smart Farming Big Data Platform for Real-Time and Batch Processing Based on Lambda Architecture," 2023. Available: https://www.aasmr.org/jsms/Vol13/No.2/Vol.13.2.2.pdf

M. Rezwanul Mahmood et al., "Machine Learning for Smart Agriculture: A Comprehensive Survey," 2024. Available: https://www.computer.org/csdl/journal/ai/2024/06/10367758/1T2CjzikifS

Md. Manowarul Islam et al., "DeepCrop: Deep learning-based crop disease prediction with web application," 2023. Available: https://www.sciencedirect.com/science/article/pii/S2666154323002715

Anusha Vangala et al., "Security in IoT-enabled Smart Agriculture: Architecture, Security Solutions and Challenges," 2022. Available: https://www.researchgate.net/publication/359259036_Security_in_IoT-enabled_Smart_Agriculture_Architecture_Security_Solutions_and_Challenges

Cătălina Mărcuță et al., "Ensuring Compliance with Regulatory Standards in Agriculture Software," 2024. Available: https://moldstud.com/articles/p-ensuring-compliance-with-regulatory-standards-in-agriculture-software

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Published

27-01-2025

Issue

Section

Research Articles

How to Cite

Multicloud-Powered Agriculture: Enhancing Precision Farming Through IoT and Data Analytics. (2025). International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 1184-1193. https://doi.org/10.32628/CSEIT251112130