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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)41-50
Number of pages10
JournalAgricultural Economics (United Kingdom)
Volume50
Issue numberS1
DOIs
StatePublished - Nov 1 2019

Bibliographical note

Funding Information:
This work was funded by the NSF awards 1838159, 1739191, and 1029711. Access to computing facilities was provided by the Minnesota Supercomputing Institute. https://drive.google.com/drive/folders/14mpxMSeOFufwIxT7GQWcUUZFsO2zvi27?usp=sharing .

Funding Information:
This work was funded by the NSF awards 1838159, 1739191, and 1029711. Access to computing facilities was provided by the Minnesota Supercomputing Institute.

Publisher Copyright:
© 2019 International Association of Agricultural Economists

Keywords

  • N5
  • Q1
  • deep learning
  • machine learning
  • monitoring crop landscapes
  • remote sensing

Fingerprint

Dive into the research topics of 'Bringing automated, remote-sensed, machine learning methods to monitoring crop landscapes at scale'. Together they form a unique fingerprint.

Cite this