000			nab a22 7a 4500
999			_c61238 _d61230
001			61238
003			MX-TxCIM
005			20200127182356.0
008			200123s2019 ne |||p|op||| 00| 0 eng d
022			_a1574-0862 (Online)
024	8		_ahttps://doi.org/10.1111/agec.12531
040			_aMX-TxCIM
041			_aeng
100	0		_911126 _aXiaowei Jia
245	1	0	_aBringing automated, remote‐sensed, machine learning methods to monitoring crop landscapes at scale
260			_aAmsterdam (Netherlands) : _bIAAE : _bWiley, _c2019.
500			_aPeer review
520			_aThis 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.
546			_aText in English
650		7	_2AGROVOC _911127 _aMachine learning
650		7	_2AGROVOC _911128 _aCrop monitoring
650		7	_2AGROVOC _91986 _aRemote sensing
700	1		_911129 _aKhandelwal, A.
700	1		_911130 _aMulla, D.J.
700	1		_99428 _aPardey, P.G.
700	1		_911131 _aKumar, V.
773	0		_dAmsterdam (Netherlands) : IAAE : Wiley, 2019. _gv. 50, S1, p. 41-50 _tAgricultural Economics _x1574-0862 _wu444456
942			_2ddc _cJA _n0