Knowledge Center Catalog

Bringing automated, remote‐sensed, machine learning methods to monitoring crop landscapes at scale (Record no. 61238)

MARC details
000 -LEADER
fixed length control field nab a22 7a 4500
001 - CONTROL NUMBER
control field 61238
003 - CONTROL NUMBER IDENTIFIER
control field MX-TxCIM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200127182356.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200123s2019 ne |||p|op||| 00| 0 eng d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 1574-0862 (Online)
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code https://doi.org/10.1111/agec.12531
040 ## - CATALOGING SOURCE
Original cataloging agency MX-TxCIM
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
100 0# - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 11126
Personal name Xiaowei Jia
245 10 - TITLE STATEMENT
Title Bringing automated, remote‐sensed, machine learning methods to monitoring crop landscapes at scale
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. Amsterdam (Netherlands) :
Name of publisher, distributor, etc. IAAE :
-- Wiley,
Date of publication, distribution, etc. 2019.
500 ## - GENERAL NOTE
General note Peer review
520 ## - SUMMARY, ETC.
Summary, etc. 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.
546 ## - LANGUAGE NOTE
Language note Text in English
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
9 (RLIN) 11127
Topical term or geographic name as entry element Machine learning
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
9 (RLIN) 11128
Topical term or geographic name as entry element Crop monitoring
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
9 (RLIN) 1986
Topical term or geographic name as entry element Remote sensing
700 1# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 11129
Personal name Khandelwal, A.
700 1# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 11130
Personal name Mulla, D.J.
700 1# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 9428
Personal name Pardey, P.G.
700 1# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 11131
Personal name Kumar, V.
773 0# - HOST ITEM ENTRY
Place, publisher, and date of publication Amsterdam (Netherlands) : IAAE : Wiley, 2019.
Related parts v. 50, S1, p. 41-50
Title Agricultural Economics
International Standard Serial Number 1574-0862
Record control number u444456
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Article
Suppress in OPAC No
Holdings
Date last seen Total Checkouts Price effective from Koha item type Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Withdrawn status Home library Current library Date acquired
01/23/2020   01/23/2020 Article Not Lost Dewey Decimal Classification     Reprints Collection   CIMMYT Knowledge Center: John Woolston Library CIMMYT Knowledge Center: John Woolston Library 01/23/2020

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