Knowledge Center Catalog

Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods (Record no. 64498)

MARC details
000 -LEADER
fixed length control field 03332nab|a22003977a|4500
001 - CONTROL NUMBER
control field 64498
003 - CONTROL NUMBER IDENTIFIER
control field MX-TxCIM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230424211951.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200910s2021||||xxk|||p|op||||00||0|eng|d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 1746-4811
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code https://doi.org/10.1186/s13007-021-00750-5
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
Personal name Juanjuan Zhang
9 (RLIN) 24663
245 10 - TITLE STATEMENT
Title Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. London (United Kingdom) :
Name of publisher, distributor, etc. BioMed Central,
Date of publication, distribution, etc. 2021.
500 ## - GENERAL NOTE
General note Peer review
500 ## - GENERAL NOTE
General note Open Access
520 ## - SUMMARY, ETC.
Summary, etc. Background: To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods: The UAV hyperspectral imaging data, Analytical Spectral Devices (ASD) data, and LAI were simultaneously obtained at main growth stages (jointing stage, booting stage, and filling stage) of various winter wheat varieties under various nitrogen fertilizer treatments. The characteristic bands related to LAI were extracted from UAV hyperspectral data with different algorithms including first derivative (FD), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS), and competitive adaptive reweighed sampling combined with successive projections algorithm (CARS_SPA). Furthermore, three modeling machine learning methods including partial least squares regression (PLSR), support vector machine regression (SVR), and extreme gradient boosting (Xgboost) were used to build LAI estimation models. Results: The results show that the correlation coefficient between UAV and ASD hyperspectral data is greater than 0.99, indicating the UAV data can be used for estimation of wheat growth information. The LAI bands selected by using different algorithms were slightly different among the 15 models built in this study. The Xgboost model using nine consecutive characteristic bands selected by CARS_SPA algorithm as input was proved to have the best performance. This model yielded identical results of coefficient of determination (0.89) for both calibration set and validation set, indicating a high accuracy of this model. Conclusions: The Xgboost modeling method in combine with CARS_SPA algorithm can reduce input variables and improve the efficiency of model operation. The results provide reference and technical support for nondestructive and rapid estimation of winter wheat LAI by using UAV.
546 ## - LANGUAGE NOTE
Language note Text in English
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
9 (RLIN) 2104
Topical term or geographic name as entry element Winter wheat
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Leaf area index
Miscellaneous information AGROVOC
9 (RLIN) 8953
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
9 (RLIN) 11401
Topical term or geographic name as entry element Unmanned aerial vehicles
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
9 (RLIN) 10231
Topical term or geographic name as entry element Imagery
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) 4859
Topical term or geographic name as entry element Models
700 0# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 24668
Personal name Tao Cheng
700 0# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 8202
Personal name Wei Guo
700 0# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 21876
Personal name Xin Xu
700 0# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 24669
Personal name Hongbo Qiao
700 0# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 24670
Personal name Yimin Xie
700 0# - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 14278
Personal name Xinming Ma
773 0# - HOST ITEM ENTRY
Title Plant Methods
Related parts v. 17, art. 49
Place, publisher, and date of publication London (United Kingdom) : BioMed Central, 2021.
International Standard Serial Number 1471-2229
Record control number 57210
856 4# - ELECTRONIC LOCATION AND ACCESS
Link text Click here to access online
Uniform Resource Identifier https://doi.org/10.1186/s13007-021-00750-5
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
11/04/2021   11/04/2021 Article Not Lost Dewey Decimal Classification     Reprints Collection   CIMMYT Knowledge Center: John Woolston Library CIMMYT Knowledge Center: John Woolston Library 11/04/2021

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