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.
Bibliographical noteFunding 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 .
This work was funded by the NSF awards 1838159, 1739191, and 1029711. Access to computing facilities was provided by the Minnesota Supercomputing Institute.
© 2019 International Association of Agricultural Economists
- deep learning
- machine learning
- monitoring crop landscapes
- remote sensing