Knowledge Center Catalog

A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram (Record no. 64509)

MARC details
000 -LEADER
fixed length control field 03375nab|a22003857a|4500
001 - CONTROL NUMBER
control field 64509
003 - CONTROL NUMBER IDENTIFIER
control field MX-TxCIM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20211108145530.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 202102s2021||||xxk|||p|op||||00||0|eng|d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 2045-2322 (Online)
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code https://doi.org/10.1038/s41598-021-92431-5
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 Xin Qi
9 (RLIN) 12677
245 12 - TITLE STATEMENT
Title A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. London (United Kingdom) :
Name of publisher, distributor, etc. Nature Publishing Group,
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. The accurate and nondestructive assessment of leaf nitrogen (N) is very important for N management in winter wheat fields. Mobile phones are now being used as an additional N diagnostic tool. To overcome the drawbacks of traditional digital camera diagnostic methods, a histogram-based method was proposed and compared with the traditional methods. Here, the field N level of six different wheat cultivars was assessed to obtain canopy images, leaf N content, and yield. The stability and accuracy of the index histogram and index mean value of the canopy images in different wheat cultivars were compared based on their correlation with leaf N and yield, following which the best diagnosis and prediction model was selected using the neural network model. The results showed that N application significantly affected the leaf N content and yield of wheat, as well as the hue of the canopy images and plant coverage. Compared with the mean value of the canopy image color parameters, the histogram could reflect both the crop coverage and the overall color information. The histogram thus had a high linear correlation with leaf N content and yield and a relatively stable correlation across different growth stages. Peak b of the histogram changed with the increase in leaf N content during the reviving stage of wheat. The histogram of the canopy image color parameters had a good correlation with leaf N content and yield. Through the neural network training and estimation model, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the estimated and measured values of leaf N content and yield were smaller for the index histogram (0.465, 9.65%, and 465.12, 5.5% respectively) than the index mean value of the canopy images (0.526, 12.53% and 593.52, 7.83% respectively), suggesting a good fit for the index histogram image color and robustness in estimating N content and yield. Hence, the use of the histogram model with a smartphone has great potential application in N diagnosis and prediction for wheat and other cereal crops.
546 ## - LANGUAGE NOTE
Language note Text in English
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
Topical term or geographic name as entry element Nitrogen content
9 (RLIN) 5218
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
Topical term or geographic name as entry element Diagnostic techniques
9 (RLIN) 24727
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
Topical term or geographic name as entry element Assessment
9 (RLIN) 8694
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Source of heading or term AGROVOC
Topical term or geographic name as entry element Image analysis
9 (RLIN) 6509
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Yanan Zhao
9 (RLIN) 24728
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Yufang Huang
9 (RLIN) 24729
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Yang Wang
9 (RLIN) 24730
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Wei Qin
9 (RLIN) 24731
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Wen Fu
9 (RLIN) 24732
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Yulong Guo
9 (RLIN) 24733
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Youliang Ye
9 (RLIN) 24734
773 0# - HOST ITEM ENTRY
Related parts v. 11, art. 13012
Place, publisher, and date of publication London : Nature Publishing Group, 2021.
International Standard Serial Number 2045-2322
Title Nature Scientific Reports
Record control number a58025
856 4# - ELECTRONIC LOCATION AND ACCESS
Link text Click here to access online
Uniform Resource Identifier https://doi.org/10.1038/s41598-021-92431-5
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
11/05/2021   11/05/2021 Article Not Lost Dewey Decimal Classification     Reprints Collection   CIMMYT Knowledge Center: John Woolston Library CIMMYT Knowledge Center: John Woolston Library 11/05/2021

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