IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v11y2025i1id779.html

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

Author

Listed:
  • Abhishek Kumar Sinha

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.

Suggested Citation

  • Abhishek Kumar Sinha, 2025. "Multicloud-Powered Agriculture: Enhancing Precision Farming Through IoT and Data Analytics," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(1), pages 1184-1193, February.
  • Handle: RePEc:jbh:ijsrcs:v11:y2025:i1:id:779
    DOI: 10.32628/CSEIT251112130
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251112130
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT251112130
    File Function: Article URL
    Download Restriction: no

    File URL: https://ijsrcseit.com/home/article/download/CSEIT251112130/CSEIT251112130
    File Function: Full text
    Download Restriction: no

    File URL: https://libkey.io/10.32628/CSEIT251112130?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v11:y2025:i1:id:779. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.