IDEAS home Printed from https://ideas.repec.org/a/eee/agisys/v216y2024ics0308521x24000362.html
   My bibliography  Save this article

D2CNN: Double-staged deep CNN for stress identification and classification in cropping system

Author

Listed:
  • Swaminathan, Bhuvaneswari
  • Vairavasundaram, Subramaniyaswamy

Abstract

Paddy crop stress can significantly reduce the quality and quantity of agricultural goods and severely affect food production safety. Untimely stress and inaccurate crop insights lead to farmers applying the wrong agricultural inputs resulting in resource wastage. It is estimated that one-third of crop damage occurs due to biotic stress, caused by any living being, and abiotic stress, caused by environmental factors. In severe cases, crop stresses can lead to no grain harvest. Therefore, the automatic detection and diagnosis of paddy crop stress is widely desired for sustainable agriculture.

Suggested Citation

  • Swaminathan, Bhuvaneswari & Vairavasundaram, Subramaniyaswamy, 2024. "D2CNN: Double-staged deep CNN for stress identification and classification in cropping system," Agricultural Systems, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:agisys:v:216:y:2024:i:c:s0308521x24000362
    DOI: 10.1016/j.agsy.2024.103886
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0308521X24000362
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agsy.2024.103886?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:agisys:v:216:y:2024:i:c:s0308521x24000362. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agsy .

    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.