Knowledge Center Catalog

High-throughput plot-level quantitative phenotyping using convolutional neural networks on very high-resolution satellite images (Record no. 67170)

MARC details
000 -LEADER
fixed length control field 02645nab|a22004097a|4500
001 - CONTROL NUMBER
control field 67170
003 - CONTROL NUMBER IDENTIFIER
control field MX-TxCIM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240919021234.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 20241s2024||||mx |||p|op||||00||0|eng|d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 2072-4292 (Online)
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code https://doi.org/10.3390/rs16020282
040 ## - CATALOGING SOURCE
Original cataloging agency MX-TxCIM
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Victor, B.
9 (RLIN) 33127
245 10 - TITLE STATEMENT
Title High-throughput plot-level quantitative phenotyping using convolutional neural networks on very high-resolution satellite images
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc. MDPI,
Date of publication, distribution, etc. 2024.
Place of publication, distribution, etc. Basel (Switzerland) :
500 ## - GENERAL NOTE
General note Peer review
500 ## - GENERAL NOTE
General note Open Access
520 ## - SUMMARY, ETC.
Summary, etc. To ensure global food security, crop breeders conduct extensive trials across various locations to discover new crop varieties that grow more robustly, have higher yields, and are resilient to local stress factors. These trials consist of thousands of plots, each containing a unique crop variety monitored at intervals during the growing season, requiring considerable manual effort. In this study, we combined satellite imagery and deep learning techniques to automatically collect plot-level phenotypes from plant breeding trials in South Australia and Sonora, Mexico. We implemented two novel methods, utilising state-of-the-art computer vision architectures, to predict plot-level phenotypes: flowering, canopy cover, greenness, height, biomass, and normalised difference vegetation index (NDVI). The first approach uses a classification model to predict for just the centred plot. The second approach predicts per-pixel and then aggregates predictions to determine a value per-plot. Using a modified ResNet18 model to predict the centred plot was found to be the most effective method. These results highlight the exciting potential for improving crop trials with remote sensing and machine learning.
546 ## - LANGUAGE NOTE
Language note Text in English
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Agriculture
Source of heading or term AGROVOC
9 (RLIN) 1007
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Image analysis
Source of heading or term AGROVOC
9 (RLIN) 6509
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Plant breeding
Miscellaneous information AGROVOC
Source of heading or term
9 (RLIN) 1203
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Remote sensing
Source of heading or term AGROVOC
9 (RLIN) 1986
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning
Source of heading or term AGROVOC
9 (RLIN) 11127
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Nibali, A.
9 (RLIN) 33128
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Newman, S.J.
9 (RLIN) 33129
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Coram, T.
9 (RLIN) 17729
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Pinto Espinosa, F.
Field link and sequence number I1707012
9 (RLIN) 4431
Miscellaneous information Formerly Global Wheat Program
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Reynolds, M.P.
Field link and sequence number INT1511
9 (RLIN) 831
Miscellaneous information Global Wheat Program
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Furbank, R.T.
9 (RLIN) 8940
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Zhen He
9 (RLIN) 10981
773 0# - HOST ITEM ENTRY
Title Remote Sensing
Related parts v. 16, no. 2, art. 282
Place, publisher, and date of publication Basel (Switzerland) : MDPI, 2024.
Record control number u57403
International Standard Serial Number 2072-4292
856 4# - ELECTRONIC LOCATION AND ACCESS
Link text Open Access through DSpace
Uniform Resource Identifier https://hdl.handle.net/10883/23043
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Article
Suppress in OPAC No
Source of classification or shelving scheme Dewey Decimal Classification
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
02/03/2024   02/03/2024 Article Not Lost Dewey Decimal Classification     CIMMYT Staff Publications Collection   CIMMYT Knowledge Center: John Woolston Library CIMMYT Knowledge Center: John Woolston Library 02/03/2024

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