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

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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Publication Details

Journal
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Published
2025-01-27
DOI
https://doi.org/10.32628/cseit251112130
Primary Topic
Smart Agriculture and AI
Type
article
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article

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

A. K. Sinha
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Smart Agriculture and AI
article

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

A. K. Sinha
article en

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.

International Journal of Scientific Research in Computer Science Engineering and Information TechnologyVol. 11(1)
St. Cloud State University (US)
Zero hunger
Openalex Percentile: Top 99%
Smart Agriculture and AI
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Multicloud-Powered Agriculture: Enhancing Precision Farming Through IoT and Data Analytics — A. K. Sinha · International Journal of Scientific Research in Computer Science Engineering and Information Technology (2025) | TGRS Research Map | TGRS