IDEAS home Printed from https://ideas.repec.org/a/bla/agecon/v50y2019is1p41-50.html
   My bibliography  Save this article

Bringing automated, remote‐sensed, machine learning methods to monitoring crop landscapes at scale

Author

Listed:
  • Xiaowei Jia
  • Ankush Khandelwal
  • David J. Mulla
  • Philip G. Pardey
  • Vipin Kumar

Abstract

This article provides an overview of how recent advances in machine learning and the availability of data from earth observing satellites can dramatically improve our ability to automatically map croplands over long periods and over large regions. It discusses three applications in the domain of crop monitoring where machine learning (ML) approaches are beginning to show great promise. For each application, it highlights machine learning challenges, proposed approaches, and recent results. The article concludes with discussion of major challenges that need to be addressed before ML approaches will reach their full potential for this problem of great societal relevance.

Suggested Citation

  • Xiaowei Jia & Ankush Khandelwal & David J. Mulla & Philip G. Pardey & Vipin Kumar, 2019. "Bringing automated, remote‐sensed, machine learning methods to monitoring crop landscapes at scale," Agricultural Economics, International Association of Agricultural Economists, vol. 50(S1), pages 41-50, November.
  • Handle: RePEc:bla:agecon:v:50:y:2019:i:s1:p:41-50
    DOI: 10.1111/agec.12531
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/agec.12531
    Download Restriction: no

    More about this item

    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:bla:agecon:v:50:y:2019:i:s1:p:41-50. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley Content Delivery). General contact details of provider: http://edirc.repec.org/data/iaaeeea.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.