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

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  • 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
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    Cited by:

    1. Julian M. Alston & Philip G. Pardey, 2020. "Innovation, Growth, and Structural Change in American Agriculture," NBER Chapters, in: The Role of Innovation and Entrepreneurship in Economic Growth, pages 123-165, National Bureau of Economic Research, Inc.

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