Rice nitrogen nutrition monitoring classification method based on the convolution neural network model: Direct detection of rice nitrogen nutritional status

The nitrogen nutrition status affects the main factors of rice yield. In traditional rice nitrogen nutrition monitoring methods, most experts enter the farmland to observe leaf color and growth and apply an appropriate amount of nitrogen fertilizer according to the results. However, this method is l...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	PloS one Ročník 17; číslo 11; s. e0273360
Hlavní autoři:	Zhai, Qiang, Ye, Chun, Li, Shuang, Liu, Jizhong, Guo, Zhiming, Chang, Ruzhi, Hua, Jing
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 22.11.2022 Public Library of Science (PLoS)
Témata:	Accuracy Agricultural land Agricultural production Algorithms Artificial neural networks Biology and Life Sciences Cameras Chlorophyll Classification Computer and Information Sciences Convolution Crop yield Datasets Deep learning Evaluation Experiments Feature extraction Fertilizers Labor Machine learning Measurement Medicine and Health Sciences Methods Modelling Monitoring Monitoring methods Neural networks Neural Networks, Computer Nitrogen Nitrogen in the body Nutrition Nutrition monitoring Nutritional aspects Nutritional Status Oryza R&D Remote sensing Research & development Research and Analysis Methods Rice Rice yield Varieties China Jiangxi China
ISSN:	1932-6203, 1932-6203
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	The nitrogen nutrition status affects the main factors of rice yield. In traditional rice nitrogen nutrition monitoring methods, most experts enter the farmland to observe leaf color and growth and apply an appropriate amount of nitrogen fertilizer according to the results. However, this method is labor- and time-consuming. To realize automatic rice nitrogen nutrition monitoring, we constructed the Jiangxi rice nitrogen nutrition monitoring model based on a convolution neural network (CNN) using the same region rice canopy image in different generation periods. Our CNN model was evaluated using multiple evaluation criteria (Accuracy, Recall, Precision, and F1 score). The results show that the same CNN model could distinguish the rice nitrogen nutrition status in different periods, which can completely realize the automatic discrimination of nitrogen nutrition status so as to guide the scientific nitrogen application of rice in this area. This will greatly improve the discrimination efficiency of the nitrogen nutrition status and reduce the time and labor cost. The application of the proposed method also proved that the CNN model can be applied in the discrimination of the nitrogen nutrition status. Among CNN models, GoogleNet model proposed a CNN architecture named Inception which can improve the depth of the network and extract higher-level features without changing the amount of calculation of the model. The GoogleNet model achieved the highest accuracy, 95.7%.
AbstractList	The nitrogen nutrition status affects the main factors of rice yield. In traditional rice nitrogen nutrition monitoring methods, most experts enter the farmland to observe leaf color and growth and apply an appropriate amount of nitrogen fertilizer according to the results. However, this method is labor- and time-consuming. To realize automatic rice nitrogen nutrition monitoring, we constructed the Jiangxi rice nitrogen nutrition monitoring model based on a convolution neural network (CNN) using the same region rice canopy image in different generation periods. Our CNN model was evaluated using multiple evaluation criteria (Accuracy, Recall, Precision, and F1 score). The results show that the same CNN model could distinguish the rice nitrogen nutrition status in different periods, which can completely realize the automatic discrimination of nitrogen nutrition status so as to guide the scientific nitrogen application of rice in this area. This will greatly improve the discrimination efficiency of the nitrogen nutrition status and reduce the time and labor cost. The application of the proposed method also proved that the CNN model can be applied in the discrimination of the nitrogen nutrition status. Among CNN models, GoogleNet model proposed a CNN architecture named Inception which can improve the depth of the network and extract higher-level features without changing the amount of calculation of the model. The GoogleNet model achieved the highest accuracy, 95.7%. The nitrogen nutrition status affects the main factors of rice yield. In traditional rice nitrogen nutrition monitoring methods, most experts enter the farmland to observe leaf color and growth and apply an appropriate amount of nitrogen fertilizer according to the results. However, this method is labor- and time-consuming. To realize automatic rice nitrogen nutrition monitoring, we constructed the Jiangxi rice nitrogen nutrition monitoring model based on a convolution neural network (CNN) using the same region rice canopy image in different generation periods. Our CNN model was evaluated using multiple evaluation criteria (Accuracy, Recall, Precision, and F1 score). The results show that the same CNN model could distinguish the rice nitrogen nutrition status in different periods, which can completely realize the automatic discrimination of nitrogen nutrition status so as to guide the scientific nitrogen application of rice in this area. This will greatly improve the discrimination efficiency of the nitrogen nutrition status and reduce the time and labor cost. The application of the proposed method also proved that the CNN model can be applied in the discrimination of the nitrogen nutrition status. Among CNN models, GoogleNet model proposed a CNN architecture named Inception which can improve the depth of the network and extract higher-level features without changing the amount of calculation of the model. The GoogleNet model achieved the highest accuracy, 95.7%.The nitrogen nutrition status affects the main factors of rice yield. In traditional rice nitrogen nutrition monitoring methods, most experts enter the farmland to observe leaf color and growth and apply an appropriate amount of nitrogen fertilizer according to the results. However, this method is labor- and time-consuming. To realize automatic rice nitrogen nutrition monitoring, we constructed the Jiangxi rice nitrogen nutrition monitoring model based on a convolution neural network (CNN) using the same region rice canopy image in different generation periods. Our CNN model was evaluated using multiple evaluation criteria (Accuracy, Recall, Precision, and F1 score). The results show that the same CNN model could distinguish the rice nitrogen nutrition status in different periods, which can completely realize the automatic discrimination of nitrogen nutrition status so as to guide the scientific nitrogen application of rice in this area. This will greatly improve the discrimination efficiency of the nitrogen nutrition status and reduce the time and labor cost. The application of the proposed method also proved that the CNN model can be applied in the discrimination of the nitrogen nutrition status. Among CNN models, GoogleNet model proposed a CNN architecture named Inception which can improve the depth of the network and extract higher-level features without changing the amount of calculation of the model. The GoogleNet model achieved the highest accuracy, 95.7%.
Audience	Academic
Author	Hua, Jing Zhai, Qiang Guo, Zhiming Li, Shuang Ye, Chun Liu, Jizhong Chang, Ruzhi
AuthorAffiliation	1 School of Mechatronic Engineering, Nanchang University, Nanchang City, Jiangxi Province, China 3 Weichai Power Co., Ltd., WeiFang City, ShanDong Province, China 4 School of Software, Jiangxi Agricultural University, Nanchang City, Jiangxi Province, China 2 Institute of Agricultural Engineering/Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Jiangxi Academy of Agricultural Sciences, Nanchang, China National University of Computer and Emerging Sciences, PAKISTAN
AuthorAffiliation_xml	– name: 1 School of Mechatronic Engineering, Nanchang University, Nanchang City, Jiangxi Province, China – name: 4 School of Software, Jiangxi Agricultural University, Nanchang City, Jiangxi Province, China – name: 2 Institute of Agricultural Engineering/Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Jiangxi Academy of Agricultural Sciences, Nanchang, China – name: National University of Computer and Emerging Sciences, PAKISTAN – name: 3 Weichai Power Co., Ltd., WeiFang City, ShanDong Province, China
Author_xml	– sequence: 1 givenname: Qiang orcidid: 0000-0001-6587-6642 surname: Zhai fullname: Zhai, Qiang – sequence: 2 givenname: Chun surname: Ye fullname: Ye, Chun – sequence: 3 givenname: Shuang surname: Li fullname: Li, Shuang – sequence: 4 givenname: Jizhong orcidid: 0000-0002-7658-5231 surname: Liu fullname: Liu, Jizhong – sequence: 5 givenname: Zhiming surname: Guo fullname: Guo, Zhiming – sequence: 6 givenname: Ruzhi surname: Chang fullname: Chang, Ruzhi – sequence: 7 givenname: Jing surname: Hua fullname: Hua, Jing
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/36413518$$D View this record in MEDLINE/PubMed
BookMark	eNqNk9tu1DAQhiNURA_wBggiISG42MWOEzvpBVJVTitVqlQOt5bjjHddvPZiOwXehYfFu5tWm2qFUC4cz3zz2_41c5wdWGchy55iNMWE4TfXrvdWmOkqhaeoYIRQ9CA7wg0pJrRA5GDn_zA7DuEaoYrUlD7KDgktMalwfZT9udIScqujd3Owue2j11E7my9dCjqv7TyXRoSglZZim4G4cF3eigBdnvZxAbl09saZfpO30Hth0hJ_Ov89CXVgTvN32oOMeQcxLWvMqdzvPzsVhyhiHx5nD5UwAZ4M60n29cP7L-efJheXH2fnZxcTSZsiTlglKqUY6gipqkI1DBokMKJt01FJ2xYJSRWqJZU1rmQJUsmuAVRXEkij6pqcZM-3uivjAh-MDTx52qAS4aJMxGxLdE5c85XXS-F_cyc03wScn3Pho5YGOMN1ukhXyQKLsqBYFKwlRFCqFCFlKZPW2-G0vl1CJ8HG5NdIdJyxesHn7oY3tMaoLpLAq0HAux89hMiXOkgwRlhw_XBvyghjCX1xD93_uoGai_QAbZVL58q1KD9jBStpUdVVoqZ7qPR1sNSpAUDpFB8VvB4VJCbCrzgXfQh89vnq_9nLb2P25Q67AGHiIgzdF8bgs12n7yy-bf8ElFtAeheCB3WHYMTXU3ZrF19PGR-mLJWd3iuTOm6GIzmizb-L_wJSrTGL
CitedBy_id	crossref_primary_10_3390_foods12061242
Cites_doi	10.1016/j.athoracsur.2004.09.040 10.1109/CVPR.2018.00474 10.1109/CVPR.2016.308 10.1109/CVPR.2018.00255 10.1016/j.knosys.2021.106923 10.1109/TIT.1967.1053964 10.1016/j.compag.2018.02.016 10.1023/A:1010933404324 10.1016/j.tplants.2015.10.015 10.1109/CVPR.2016.90 10.1080/00103629209368733 10.1016/j.ijin.2020.11.001 10.1023/A:1009715923555 10.1016/j.compag.2016.04.024
ContentType	Journal Article
Copyright	Copyright: © 2022 Zhai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2022 Public Library of Science 2022 Zhai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 Zhai et al 2022 Zhai et al
Copyright_xml	– notice: Copyright: © 2022 Zhai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. – notice: COPYRIGHT 2022 Public Library of Science – notice: 2022 Zhai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 Zhai et al 2022 Zhai et al
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM IOV ISR 3V. 7QG 7QL 7QO 7RV 7SN 7SS 7T5 7TG 7TM 7U9 7X2 7X7 7XB 88E 8AO 8C1 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AEUYN AFKRA ARAPS ATCPS AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU D1I DWQXO FR3 FYUFA GHDGH GNUQQ H94 HCIFZ K9. KB. KB0 KL. L6V LK8 M0K M0S M1P M7N M7P M7S NAPCQ P5Z P62 P64 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PTHSS PYCSY RC3 7X8 5PM DOA
DOI	10.1371/journal.pone.0273360
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Opposing Viewpoints Gale In Context: Science ProQuest Central (Corporate) Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Biotechnology Research Abstracts Nursing & Allied Health Database Ecology Abstracts Entomology Abstracts (Full archive) Immunology Abstracts Meteorological & Geoastrophysical Abstracts Nucleic Acids Abstracts Virology and AIDS Abstracts Agricultural Science Collection Proquest Health and Medical Complete ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Public Health Database Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection Agricultural & Environmental Science Collection ProQuest Central Essentials Biological Science Database ProQuest Central Technology collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Materials Science Collection ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student AIDS and Cancer Research Abstracts SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Materials Science Database Nursing & Allied Health Database (Alumni Edition) Meteorological & Geoastrophysical Abstracts - Academic ProQuest Engineering Collection ProQuest Biological Science Collection Agricultural Science Database Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Algology Mycology and Protozoology Abstracts (Microbiology C) Biological Science Database Engineering Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Environmental Science Database Materials Science Collection ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering collection Environmental Science Collection Genetics Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Open Access Full Text
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Agricultural Science Database Publicly Available Content Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Meteorological & Geoastrophysical Abstracts Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection Advanced Technologies & Aerospace Collection Engineering Database Virology and AIDS Abstracts ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Agricultural Science Collection ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Ecology Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Environmental Science Collection Entomology Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Environmental Science Database ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic Meteorological & Geoastrophysical Abstracts - Academic ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Materials Science Collection ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Genetics Abstracts ProQuest Engineering Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) Agricultural & Environmental Science Collection AIDS and Cancer Research Abstracts Materials Science Database ProQuest Materials Science Collection ProQuest Public Health ProQuest Nursing & Allied Health Source ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Animal Behavior Abstracts Materials Science & Engineering Collection Immunology Abstracts ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic MEDLINE Agricultural Science Database CrossRef
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Sciences (General)
DocumentTitleAlternate	Effectiveness of using the convolution neural network model to monitor rice nitrogen nutrition
EISSN	1932-6203
ExternalDocumentID	2739040124 oai_doaj_org_article_718d33d5c21a4261a27b33a66ff3344c PMC9681082 A727462585 36413518 10_1371_journal_pone_0273360
Genre	Research Support, Non-U.S. Gov't Journal Article
GeographicLocations	China Jiangxi China
GeographicLocations_xml	– name: China – name: Jiangxi China
GrantInformation_xml	– fundername: ; grantid: 61863027 – fundername: ; grantid: 20202BBFL63046 – fundername: ; grantid: 20202BBE53025
GroupedDBID	--- 123 29O 2WC 53G 5VS 7RV 7X2 7X7 7XC 88E 8AO 8C1 8CJ 8FE 8FG 8FH 8FI 8FJ A8Z AAFWJ AAUCC AAWOE AAYXX ABDBF ABIVO ABJCF ABUWG ACCTH ACGFO ACIHN ACIWK ACPRK ACUHS ADBBV AEAQA AENEX AEUYN AFFHD AFKRA AFPKN AFRAH AHMBA ALMA_UNASSIGNED_HOLDINGS AOIJS APEBS ARAPS ATCPS BAIFH BAWUL BBNVY BBTPI BCNDV BENPR BGLVJ BHPHI BKEYQ BPHCQ BVXVI BWKFM CCPQU CITATION CS3 D1I D1J D1K DIK DU5 E3Z EAP EAS EBD EMOBN ESX EX3 F5P FPL FYUFA GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IAO IEA IGS IHR IHW INH INR IOV IPY ISE ISR ITC K6- KB. KQ8 L6V LK5 LK8 M0K M1P M48 M7P M7R M7S M~E NAPCQ O5R O5S OK1 OVT P2P P62 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PTHSS PV9 PYCSY RNS RPM RZL SV3 TR2 UKHRP WOQ WOW ~02 ~KM 3V. ADRAZ ALIPV BBORY CGR CUY CVF ECM EIF IPNFZ NPM RIG 7QG 7QL 7QO 7SN 7SS 7T5 7TG 7TM 7U9 7XB 8FD 8FK AZQEC C1K DWQXO ESTFP FR3 GNUQQ H94 K9. KL. M7N P64 PKEHL PQEST PQUKI PRINS RC3 7X8 PUEGO 5PM AAPBV ABPTK BBAFP N95
ID	FETCH-LOGICAL-c692t-75a5ff70d33552f97e90a106b9d6c6bb0ac6f08c6c815c4ecfcd9e085ce39f883
IEDL.DBID	DOA
ISICitedReferencesCount	2
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000903522800005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1932-6203
IngestDate	Mon Dec 05 23:08:10 EST 2022 Fri Oct 03 12:44:27 EDT 2025 Tue Nov 04 02:09:24 EST 2025 Thu Oct 02 10:07:33 EDT 2025 Tue Oct 07 07:54:32 EDT 2025 Sat Nov 29 13:05:58 EST 2025 Sat Nov 29 10:06:56 EST 2025 Wed Nov 26 09:38:16 EST 2025 Wed Nov 26 09:24:35 EST 2025 Thu May 22 21:22:55 EDT 2025 Wed Feb 19 02:26:27 EST 2025 Sat Nov 29 05:48:19 EST 2025 Tue Nov 18 21:17:25 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	11
Language	English
License	Copyright: © 2022 Zhai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Creative Commons Attribution License
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c692t-75a5ff70d33552f97e90a106b9d6c6bb0ac6f08c6c815c4ecfcd9e085ce39f883
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist.
ORCID	0000-0002-7658-5231 0000-0001-6587-6642
OpenAccessLink	https://doaj.org/article/718d33d5c21a4261a27b33a66ff3344c
PMID	36413518
PQID	2739040124
PQPubID	1436336
PageCount	e0273360
ParticipantIDs	plos_journals_2739040124 doaj_primary_oai_doaj_org_article_718d33d5c21a4261a27b33a66ff3344c pubmedcentral_primary_oai_pubmedcentral_nih_gov_9681082 proquest_miscellaneous_2739067377 proquest_journals_2739040124 gale_infotracmisc_A727462585 gale_infotracacademiconefile_A727462585 gale_incontextgauss_ISR_A727462585 gale_incontextgauss_IOV_A727462585 gale_healthsolutions_A727462585 pubmed_primary_36413518 crossref_primary_10_1371_journal_pone_0273360 crossref_citationtrail_10_1371_journal_pone_0273360
PublicationCentury	2000
PublicationDate	2022-11-22
PublicationDateYYYYMMDD	2022-11-22
PublicationDate_xml	– month: 11 year: 2022 text: 2022-11-22 day: 22
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: San Francisco – name: San Francisco, CA USA
PublicationTitle	PloS one
PublicationTitleAlternate	PLoS One
PublicationYear	2022
Publisher	Public Library of Science Public Library of Science (PLoS)
Publisher_xml	– name: Public Library of Science – name: Public Library of Science (PLoS)
References	S Azimi (pone.0273360.ref013) 2020; 173 Zhou Chunyan (pone.0273360.ref002) 2019 L. Breiman (pone.0273360.ref006) 2001; 45 TM Cover (pone.0273360.ref007) 2003; 13 C. Burges (pone.0273360.ref008) 1998; 2 pone.0273360.ref019 BQ Li (pone.0273360.ref016) 2021 MB Assad (pone.0273360.ref022) 2020; 1 K Simonyan (pone.0273360.ref024) 2014 Y Li (pone.0273360.ref025) 2016; 80 X Yan (pone.0273360.ref001) 2008 Andreas Kamilaris (pone.0273360.ref012) 2018; 147 A Singh (pone.0273360.ref011) 2016; 21 KM Veley (pone.0273360.ref014) 2017 pone.0273360.ref009 E Hamuda (pone.0273360.ref010) 2016; 125 pone.0273360.ref020 pone.0273360.ref021 JS Schepers (pone.0273360.ref004) 1992; 23 W Rawat (pone.0273360.ref017) 2017 pone.0273360.ref023 M Debtanu (pone.0273360.ref003) 2004 J A Hanley (pone.0273360.ref027) 1983 CM Jones (pone.0273360.ref028) 2005; 79 C Szegedy (pone.0273360.ref018) 2014 B. Liu (pone.0273360.ref026) 2021; 220 S L. Osborne (pone.0273360.ref005) 2013 J An (pone.0273360.ref015) 2019
References_xml	– year: 2017 ident: pone.0273360.ref014 article-title: High-throughput profiling and analysis of plant responses over time to abiotic stress publication-title: Plant Direct – volume: 79 start-page: 16 issue: 1 year: 2005 ident: pone.0273360.ref028 article-title: Summary Receiver Operating Characteristic Curve Analysis Techniques in the Evaluation of Diagnostic Tests publication-title: Annals of Thoracic Surgery doi: 10.1016/j.athoracsur.2004.09.040 – ident: pone.0273360.ref021 doi: 10.1109/CVPR.2018.00474 – ident: pone.0273360.ref023 doi: 10.1109/CVPR.2016.308 – ident: pone.0273360.ref019 doi: 10.1109/CVPR.2018.00255 – volume: 220 start-page: 106923 issue: 1 year: 2021 ident: pone.0273360.ref026 article-title: Understanding the loss landscape of one-hidden-layer ReLU networks publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2021.106923 – year: 2019 ident: pone.0273360.ref002 article-title: Imaging analysis of chlorophyll fluorescence induction for monitoring plant water and nitrogen treatments publication-title: Measurement – year: 2014 ident: pone.0273360.ref024 article-title: Very Deep Convolutional Networks for Large-Scale Image Recognition publication-title: Computer Science – volume: 13 start-page: 21 issue: 1 year: 2003 ident: pone.0273360.ref007 article-title: Nearest neighbor pattern classification publication-title: IEEE Transactions on Information Theory doi: 10.1109/TIT.1967.1053964 – volume: 147 start-page: 70 year: 2018 ident: pone.0273360.ref012 article-title: Deep learning in agriculture: A survey publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2018.02.016 – volume: 45 start-page: 5 year: 2001 ident: pone.0273360.ref006 article-title: Random forest publication-title: Machine Learning doi: 10.1023/A:1010933404324 – volume: 21 start-page: 110 issue: 2 year: 2016 ident: pone.0273360.ref011 article-title: Machine Learning for High-Throughput Stress Phenotyping in Plants publication-title: Trends in Plant Science doi: 10.1016/j.tplants.2015.10.015 – year: 2017 ident: pone.0273360.ref017 article-title: Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review publication-title: Neural Computation – start-page: 450 issue: 02 year: 2008 ident: pone.0273360.ref001 article-title: Recent Advances in Technology of Increasing Fertilizer Use Efficiency publication-title: Scientia Agricultura Sinica – ident: pone.0273360.ref020 doi: 10.1109/CVPR.2016.90 – year: 2013 ident: pone.0273360.ref005 article-title: Determining Nitrogen Nutrition and Yield of Canola Through Existing Remote Sensing Technology publication-title: Agricultural Journal – year: 1983 ident: pone.0273360.ref027 article-title: A method of comparing the areas under receiver operating characteristic curves derived from the same cases publication-title: Radiology – year: 2014 ident: pone.0273360.ref018 article-title: Going Deeper with Convolutions publication-title: IEEE Computer Society – volume: 23 start-page: 2173 issue: 17–20 year: 1992 ident: pone.0273360.ref004 article-title: Comparison of corn leaf nitrogen concentration and chlorophyll meter readings publication-title: Communications in Soil Science & Plant Analysis doi: 10.1080/00103629209368733 – volume: 1 start-page: 148 year: 2020 ident: pone.0273360.ref022 article-title: Deep Biomedical Image Classification Using Diagonal Bilinear Interpolation and residual network publication-title: International Journal of Intelligent Networks doi: 10.1016/j.ijin.2020.11.001 – volume: 80 year: 2016 ident: pone.0273360.ref025 article-title: Revisiting Batch Normalization For Practical Domain Adaptation publication-title: Pattern Recognition – year: 2019 ident: pone.0273360.ref015 article-title: Identification and Classification of Maize Drought Stress Using Deep Convolutional Neural Network publication-title: Symmetry – volume: 2 start-page: 121 issue: 2 year: 1998 ident: pone.0273360.ref008 article-title: A Tutorial on Support Vector Machines for Pattern Recognition publication-title: Data Mining and Knowledge Discovery doi: 10.1023/A:1009715923555 – volume: 173 start-page: 108650 issue: 15 year: 2020 ident: pone.0273360.ref013 article-title: A deep learning approach to measure stress level in plants due to Nitrogen deficiency publication-title: Measurement – year: 2004 ident: pone.0273360.ref003 article-title: Management of nitrogen through the use of leaf color chart (LCC) and soil plant analysis development (SPAD) or chlorophyll meter in rice under irrigated ecosystem publication-title: The Scientific World Journal – volume: 125 start-page: 184 year: 2016 ident: pone.0273360.ref010 article-title: A survey of image processing techniques for plant extraction and segmentation in the field publication-title: Computers & Electronics in Agriculture doi: 10.1016/j.compag.2016.04.024 – start-page: 8 issue: 04 year: 2021 ident: pone.0273360.ref016 article-title: Review of convolutional neural networks publication-title: Computer ERA – ident: pone.0273360.ref009
SSID	ssj0053866
Score	2.4343894
Snippet	The nitrogen nutrition status affects the main factors of rice yield. In traditional rice nitrogen nutrition monitoring methods, most experts enter the...
SourceID	plos doaj pubmedcentral proquest gale pubmed crossref
SourceType	Open Website Open Access Repository Aggregation Database Index Database Enrichment Source
StartPage	e0273360
SubjectTerms	Accuracy Agricultural land Agricultural production Algorithms Artificial neural networks Biology and Life Sciences Cameras Chlorophyll Classification Computer and Information Sciences Convolution Crop yield Datasets Deep learning Evaluation Experiments Feature extraction Fertilizers Labor Machine learning Measurement Medicine and Health Sciences Methods Modelling Monitoring Monitoring methods Neural networks Neural Networks, Computer Nitrogen Nitrogen in the body Nutrition Nutrition monitoring Nutritional aspects Nutritional Status Oryza R&D Remote sensing Research & development Research and Analysis Methods Rice Rice yield Varieties
SummonAdditionalLinks	– databaseName: Biological Science Database dbid: M7P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZg4cAFKK-mFDAICTikTeLEsbmggqjgUqryUG-R40epVJLtZpdfw49lxnFSglaAxHHX483anvk844y_IeQpIByvndOxNS6LcyNULHWZxEKz2grBueN9sYny4EAcH8vDcODWhbTKARM9UJtW4xn5LmyzEhQOtqNX8_MYq0bh29VQQuMyuYIsCZlP3TsckBhsmfNwXY6V6W5YnZ1529gd5HFhnpjyYjvyrP0jNs_mZ223zvH8PX_ylw1p_8b_DuUmuR5cUbrX684GuWSbW2QjGHtHnwdG6he3yY8jgBMKxr9oQd9oMzD4028eEfBokGp0wzHvSPUtvjI1xU3SUPgMfibFDPeg6RR5NOHhTZ-FTn1Bnpe0B2Bq7NJniDW0dXSx_tnQGS9Drbo75PP-209v3sWhrEOsucyWcVmowrkyMQx8nczJ0spEQWRaS8M1r-tEae4SobkWaaFzq5020oJrqC2TTgh2l8waWMJNQp1VHGL7uhZFntepVtIUNjUyccZg4BoRNqxupQPnOZbeOKv8i7wSYp9-rivUiSroRETisde85_z4i_xrVJxRFhm7_Rft4qQKAFCBDwADNoXOUoVhq8rKmjEFxuAYy3MdkUeodlV__XXEnWoPHMwcglRRROSJl0DWjgbTgk7Uquuq9x--_IPQx6OJ0LMg5FqYDq3CVQwYE7KBTSS3J5KAPXrSvIlGMsxKV12oNvQclH998-OxGX8UU_0a266CDFZPKiNyr7ezcWYZz7GipIhIObHAydRPW5rTr540XSLxnsi2_vy37pNrGd5vSdM4y7bJbLlY2Qfkqv6-PO0WDz26_ATNm4o- priority: 102 providerName: ProQuest – databaseName: Public Library of Science (PLoS) Journals Open Access dbid: FPL link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9QwDI_QwQMvwPhatwEBIQEP3dqkTRPeBuIEEhrT-NDeqjQfMGm0p-sdfw1_LHabFjrdBDze2bm2ju3YV_tnQp6ChxOV9yZ21rM4s1LHyhRJLA2vnJRCeNEPmyiOjuTpqTr-nSheeIPPi_QgyHR_0dRuH9FXuIAU_SrjQuCohvnx-8Hzgu0KEdrjLls5OX46lP7RF88W5027KdC8WC_5xwE0v_m_t36L3AihJj3sdWOLXHH1bbIVjLmlzwPi9Is75OcJuAsKxr1sQJ9oPSD00--dxeNff9RgmI11RbqndJOnKR6ClsJniCMpVrAHTaaIkwkXr_sqc9oN3HlJewdLrVt1FWA1bTxdbr42LMZmp3V7l3yev_n0-m0cxjbERii2iotc594XieUQyzCvCqcSDZlnpawwoqoSbYRPpBFGprnJnPHGKgehn3FceSn5PTKrQWLbhHqnBeTuVSXzLKtSo5XNXWpV4q3FxDQifNjN0gRMcxytcV52L-oKyG16WZe4BWXYgojE46pFj-nxF_5XqCgjLyJyd1_AXpfBwEs44-GBbW5YqjEt1ayoONeg7J7zLDMReYRqVvbtraNfKQ8hgMwgCZV5RJ50HIjKUWPZz1e9btvy3Ycv_8D08WTC9Cww-QbEYXRotYBnQrSvCefehBN8i5mQt9EoBqm0JQhEgduHoBBWDoaymfx4JOOPYilf7Zp14MHpSEVE7vd2NUqWiwwnRsqIFBOLm4h-SqnPvnWg6AqB9STbufyOd8l1hr0raRoztkdmq-XaPSDXzI_VWbt82HmSXysteJU priority: 102 providerName: Public Library of Science
Title	Rice nitrogen nutrition monitoring classification method based on the convolution neural network model: Direct detection of rice nitrogen nutritional status
URI	https://www.ncbi.nlm.nih.gov/pubmed/36413518 https://www.proquest.com/docview/2739040124 https://www.proquest.com/docview/2739067377 https://pubmed.ncbi.nlm.nih.gov/PMC9681082 https://doaj.org/article/718d33d5c21a4261a27b33a66ff3344c http://dx.doi.org/10.1371/journal.pone.0273360
Volume	17
WOSCitedRecordID	wos000903522800005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: DOA dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M~E dateStart: 20060101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Agricultural Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M0K dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/agriculturejournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M7P dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M7S dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Environmental Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: PATMY dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/environmentalscience providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 7X7 dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Materials Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: KB. dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/materialsscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 7RV dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest advanced technologies & aerospace journals customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: P5Z dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: BENPR dateStart: 20061201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Public Health Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 8C1 dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/publichealth providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: PIMPY dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVATS databaseName: Public Library of Science (PLoS) Journals Open Access customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: FPL dateStart: 20060101 isFulltext: true titleUrlDefault: http://www.plos.org/publications/ providerName: Public Library of Science
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9NAEF5B4MAFUV4NlLAgJODg1Pba--DWVK2oSoOVQlW4WPZ6FyoVJ4oTfg0_lpn1JtQoUjlwGSnZWTuZl2eS2W8IeQURjpfW6sBUNg6SShaB0iIMpGalkZJzy9thE2I8lufnKrsy6gt7wlp44FZwuxA7K8aqVMdRgel-EYuSsQIuYhlLEo3RNxRqVUy1MRi8mHN_UI6JaNfrZTib1maICC7MQVL-eRA5vP51VO7NLqfNppTz787JK4-iw3vkrs8h6V772bfIDVPfJ1veSxv6xkNJv31Afk0gDlDw2vkUDIXWK-h9-sO5Mv6mRzXmz9gwVLQrbqQ0xadbReE1JIgUW9O9iVIEwISb1237OHWTdN7RNnLSyixca1dNp5bON98bNuMppmXzkHw-PPi0_z7w8xgCzVW8CERapNaKEPSRprFVwqiwgJKyVBXXvCzDQnMbSs21jFKdGG11pQzkdNowZaVkj0ivBg1sE2pNwaEoL0uZJkkZ6UJVqYkqFdqqwoqzT9hKObn2YOU4M-Myd__ACShaWlnnqNLcq7RPgvWuWQvWcQ3_CPW-5kWobfcGGGDuDTC_zgD75DlaTd6eW10HjHwPMsMEqkuZ9slLx4FwGzX283wrlk2TH308-wem00mH6bVnslMQhy78GQr4Tgjj1eHc6XBC0NCd5W208ZVUmhwEoiCeQ7YHO1d2v3n5xXoZL4o9erWZLj0Pjj0SffK4dZO1ZBlPcBSk7BPRcaCO6Lsr9cV3h3auEDFPxk_-h66ekjsxHl-JoiCOd0hvMV-aZ-S2_rm4aOYDclNMzpCeC0clULkfDcit0cE4mwxciAF6mH0AejwaAj0Jj5GKzNFToFn6FXZkRyfZl98UQYgj
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VgAQXoLxqKHRBIODg1s_1Ggmh8qgapQ2oFNSbWe-jVCp2iBMQ_4XfwG9kxl67GEXApQeO9s7a8WTmmxl7HoTcB4RjuTHS1coEbqS4cFOZeC6XYa45Z8ywZthEMh7zg4P0zRL50dbCYFpli4k1UKtS4jvyDTCzKQgcmKNnk88uTo3Cr6vtCI1GLEb621cI2aqnw5fw_z4Igq1X-y-2XTtVwJUsDWZuEovYmMRTIZjawKSJTj0BgVGeKiZZnntCMuNxyST3YxlpaaRKNXgmUoep4TyE654hZyMIvDCFbNcbtcgP2MGYLc8LE3_DSsP6pCz0OvaNCetGmCfmr54S0NmCweS4rBY5ur_na_5iALcu_W-su0wuWlebbja6sUyWdHGFLFswq-gj23H78VXyfQ_gkgK4TUvQJ1q0Ewropxrx8NUnlRhmYF6VaFbqydsUnQBF4Rj8aIoZ_FaTKfYJhZsXTZY9rQcOPaGNgaFKz-oMuIKWhk4X3xs2Y7HXvLpG3p0Kl66TQQEis0Ko0YIlUZDnPI6i3JciVbH2VeoZpTAwd0jYSlMmbU93HC1ynNUfKhOI7RpeZyiDmZVBh7jdrknT0-Qv9M9RUDta7Ehenyinh5kFuAx8HHhgFcvAFxiWiyDJw1CAspswjCLpkDUU86wp7-1wNdsEBzqCIJzHDrlXU2BXkgLTng7FvKqy4ev3_0D0dq9H9NASmRLYIYUtNYFnwm5nPcrVHiVgq-wtr6BStlypshNVgp2tsi1evtst40UxlbHQ5dzS4HSoxCE3Gr3uOBuyCCdmcockPY3vsb6_Uhx9rJvCp9hYkAc3__yz1sj57f3dnWxnOB7dIhcCrOXxfTcIVslgNp3r2-Sc_DI7qqZ3amSj5MNp48FP9mboyg
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VgBAXoLwaKHRBIODgxl7b6zUSQoUSERWFqgXUm7H3USoVO8QJiP_CL-HXMbNepxhFwKUHjvbO2vFk5psZex6E3AeE44Ux0tPKMC9SIvdSmfiekGGhheDc8GbYRDIei4ODdHeF_GhrYTCtssVEC9SqkviOfABmNgWBA3M0MC4tYnd7-Gzy2cMJUviltR2n0YjIjv72FcK3-uloG_7rB4wNX7598cpzEwY8yVM285I4j41JfBWC2WUmTXTq5xAkFanikheFn0tufCG5FEEsIy2NVKkGL0XqMDVChHDdM-RsEnGf2bTB3dYKAI5w7kr1wiQYOMnYnFSl3sQeMqFtinliCu3EgIVd6E2Oq3qZ0_t77uYvxnB46X9m42Vy0bngdKvRmVWyossrZNWBXE0fuU7cj6-S73sAoxRAb1qBntGynVxAP1kkxFeiVGL4gflWebNiJ3JTdA4UhWPwrylm9jsNp9g_FG5eNtn31A4iekIbw0OVntnMuJJWhk6X3xs2YxHYvL5G3p0Kl66TXgnis0ao0TlPIlYUIo6iIpB5qmIdqNQ3SmHA3idhK1mZdL3eceTIcWY_YCYQ8zW8zlAeMyePfeItdk2aXid_oX-OQrugxU7l9kQ1Pcwc8GXg-8ADq1iyIMdwPWdJEYY5gIAJwyiSfbKBIp81Zb8LvM22wLGOIDgXcZ_csxTYraREgT3M53Wdjd68_wei_b0O0UNHZCpgh8xdCQo8E3ZB61CudygBc2VneQ0VtOVKnZ2oFexsFW_58t3FMl4UUxxLXc0dDU6NSvrkRqPjC86GPMJJmqJPko72d1jfXSmPPtpm8Sk2HBTs5p9_1gY5DzCQvR6Nd26RCwxLfILAY2yd9GbTub5Nzskvs6N6eseCHCUfThsOfgLIa_Gh
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Rice+nitrogen+nutrition+monitoring+classification+method+based+on+the+convolution+neural+network+model%3A+Direct+detection+of+rice+nitrogen+nutritional+status&rft.jtitle=PloS+one&rft.au=Zhai%2C+Qiang&rft.au=Ye%2C+Chun&rft.au=Li%2C+Shuang&rft.au=Liu%2C+Jizhong&rft.date=2022-11-22&rft.issn=1932-6203&rft.eissn=1932-6203&rft.volume=17&rft.issue=11&rft.spage=e0273360&rft_id=info:doi/10.1371%2Fjournal.pone.0273360&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1932-6203&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1932-6203&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1932-6203&client=summon