TY  - JA
AU  - Xiaowei Jia
AU  - Khandelwal,A.
AU  - Mulla,D.J.
AU  - Pardey,P.G.
AU  - Kumar,V.
TI  - Bringing automated, remote‐sensed, machine learning methods to monitoring crop landscapes at scale
SN  - 1574-0862
PY  - 2019///
CY  - Amsterdam (Netherlands)
PB  - IAAE, Wiley
KW  - AGROVOC
KW  - Machine learning
KW  - Crop monitoring
KW  - Remote sensing
N1  - Peer review
N2  - 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
DO  - https://doi.org/10.1111/agec.12531
T2  - Agricultural Economics
ER  -