Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System

Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-pa...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 13; číslo 3; s. 401
Hlavní autoři:	Cummings, Cadan, Miao, Yuxin, Paiao, Gabriel Dias, Kang, Shujiang, Fernández, Fabián G.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.01.2021
Témata:	aboveground biomass active canopy sensing Air temperature Atmospheric pressure Biomass Canopies canopy Cell division Chlorophyll Corn Crops developmental stages Diagnosis Drainage Drainage systems experimental design Farms Fertilizers field experimentation Growing season Growth stage Humidity Information management integrated sensing system Leaf area Leaf area index Learning algorithms Machine learning Minnesota model validation Modelling Nitrogen nitrogen content nitrogen nutrition index no-tillage normalized difference vegetation index Normalized difference vegetative index Nutrition Nutrition assessment Parameters Performance prediction phenomics photosynthetically active radiation Plant growth precision nitrogen management reflectance Regression analysis Regression models Remote sensing Seasons Sensors soil Soils strip tillage Tillage Vegetables Vegetation Vegetation index Zea mays United States > US Minnesota
ISSN:	2072-4292, 2072-4292
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha−1 increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/−1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (ΔTemp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models.
AbstractList	Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha−1 increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/−1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (ΔTemp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models. Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha⁻¹ increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/-1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (ΔTemp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models.
Author	Paiao, Gabriel Dias Cummings, Cadan Kang, Shujiang Fernández, Fabián G. Miao, Yuxin
Author_xml	– sequence: 1 givenname: Cadan surname: Cummings fullname: Cummings, Cadan – sequence: 2 givenname: Yuxin orcidid: 0000-0001-8419-6511 surname: Miao fullname: Miao, Yuxin – sequence: 3 givenname: Gabriel Dias surname: Paiao fullname: Paiao, Gabriel Dias – sequence: 4 givenname: Shujiang surname: Kang fullname: Kang, Shujiang – sequence: 5 givenname: Fabián G. orcidid: 0000-0002-9539-0050 surname: Fernández fullname: Fernández, Fabián G.
BookMark	eNptkV1LHTEQhoNYqFpv_AUBb0RYO_nYj1yWra0HbCuceh2ys9ljDrvJaZK1-O-79ZRWpHMzw_DMyzu8x-TQB28JOWNwJYSC9zExAQIksANyxKHmheSKH76Y35LTlLawlBBMgTwi2Ibo6VeXY9hYT9fZ5DnRj85sfEgu0Z8uP1Dj6cr78Giye7T0yzxmV9yZaCabbaRtDDvauoijpXcP1oeJrq1Pzm_o-illO70jbwYzJnv6p5-Q-0_X39ub4vbb51X74bZAoWQuhsbyrmaAneDITQPYI0DZGaOs6CyrqnJoJPJKmgUQ3dALKCXrscSSDdiLE7La6_bBbPUuusnEJx2M08-LEDfaxOwWn7pTTPEKyhqAyZ73DYLEWmAjq543OCxaF3utXQw_ZpuynlxCO47G2zAnzcuSqUpKphb0_BW6DXP0y6eay4ZLWTfP1OWewhhSinb4a5CB_h2f_hffAsMrGN2SjAs-R-PG_538AnMTnZI
CitedBy_id	crossref_primary_10_1016_j_eja_2025_127629 crossref_primary_10_1155_2024_8874325 crossref_primary_10_1149_1945_7111_ad22d8 crossref_primary_10_3390_agronomy12030555 crossref_primary_10_1016_j_agrformet_2021_108564 crossref_primary_10_1002_agj2_20892 crossref_primary_10_3389_fpls_2022_890892 crossref_primary_10_1007_s11119_021_09869_w crossref_primary_10_1016_j_indcrop_2024_118627 crossref_primary_10_3390_rs17050743 crossref_primary_10_1007_s11056_024_10083_5 crossref_primary_10_1016_j_rse_2022_113141 crossref_primary_10_1002_moda_70018 crossref_primary_10_3390_rs14010120 crossref_primary_10_3390_agriengineering6040233 crossref_primary_10_3390_rs14020394 crossref_primary_10_1016_j_jia_2023_02_027 crossref_primary_10_1042_ETLS20200275 crossref_primary_10_1080_2150704X_2023_2282400 crossref_primary_10_1016_j_jag_2021_102416 crossref_primary_10_1016_j_fcr_2023_108844 crossref_primary_10_3390_agriculture13040835 crossref_primary_10_1016_j_compag_2024_108993 crossref_primary_10_1016_j_compag_2024_109565 crossref_primary_10_3389_fpls_2024_1319938 crossref_primary_10_1016_j_compag_2022_106998
Cites_doi	10.1145/2939672.2939785 10.2134/agronj2008.0016 10.1002/agj2.20248 10.2134/agronj2009.0021 10.1016/j.agwat.2015.03.023 10.2134/agronj2019.04.0309 10.1177/001316446002000104 10.3390/rs6076549 10.1016/j.fcr.2012.06.003 10.1038/s41592-019-0686-2 10.1007/BF00189456 10.2134/agronj2011.0124 10.1007/s11119-016-9431-3 10.3390/rs12020215 10.1016/j.compag.2019.104872 10.1016/j.fcr.2008.06.013 10.3390/rs8070605 10.1007/s11119-008-9091-z 10.1016/j.fcr.2013.12.018 10.1038/s41598-017-14597-1 10.4081/ija.2009.4.147 10.1016/j.eja.2017.12.006 10.2307/2529310 10.1126/science.1183899 10.1007/s13593-012-0111-z 10.1007/s10705-016-9810-1 10.2135/cropsci2000.403723x 10.3390/rs71114939 10.1023/A:1004783431055 10.2134/jeq2000.00472425002900040009x 10.1016/j.eja.2008.05.007 10.2307/1936256 10.1016/j.ecolmodel.2019.06.002 10.1109/Agro-Geoinformatics.2016.7577610 10.1029/WR017i004p01133 10.1017/S0021859610000018 10.1016/j.fcr.2017.09.033 10.1094/CM-2009-1211-01-RS 10.2134/agronj2017.12.0733 10.3390/su9081339 10.3390/rs70810646 10.1016/j.compag.2018.05.012 10.3390/rs10122026 10.2134/agronj2006.0135 10.1016/j.fcr.2010.01.010
ContentType	Journal Article
Copyright	2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: 2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD 8FE 8FG ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ BHPHI BKSAR C1K CCPQU DWQXO F28 FR3 H8D H8G HCIFZ JG9 JQ2 KR7 L6V L7M L~C L~D M7S P5Z P62 P64 PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 7S9 L.6 DOA
DOI	10.3390/rs13030401
DatabaseName	CrossRef Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Ecology Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Materials Science & Engineering ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology collection Natural Science Collection Earth, Atmospheric & Aquatic Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Central ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Engineering Database ProQuest advanced technologies & aerospace journals ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) 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 AGRICOLA AGRICOLA - Academic DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database Materials Research Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China Materials Business File Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences Engineered Materials Abstracts Natural Science Collection Chemoreception Abstracts ProQuest Central (New) Engineering Collection ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Engineering Database Aluminium Industry Abstracts ProQuest One Academic Eastern Edition Electronics & Communications Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection Ceramic Abstracts Ecology Abstracts Biotechnology and BioEngineering Abstracts ProQuest One Academic UKI Edition Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Central (Alumni Edition) ProQuest One Community College Earth, Atmospheric & Aquatic Science Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Engineering Collection Biotechnology Research Abstracts ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Materials Science & Engineering Collection Corrosion Abstracts AGRICOLA AGRICOLA - Academic
DatabaseTitleList	Publicly Available Content Database AGRICOLA 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: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography
EISSN	2072-4292
ExternalDocumentID	oai_doaj_org_article_b919260570014d2d8c04c73c846d28cf 10_3390_rs13030401
GeographicLocations	United States--US Minnesota
GeographicLocations_xml	– name: Minnesota – name: United States--US
GroupedDBID	29P 2WC 2XV 5VS 8FE 8FG 8FH AADQD AAHBH AAYXX ABDBF ABJCF ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ BHPHI BKSAR CCPQU CITATION E3Z ESX FRP GROUPED_DOAJ HCIFZ I-F IAO ITC KQ8 L6V LK5 M7R M7S MODMG M~E OK1 P62 PCBAR PHGZM PHGZT PIMPY PQGLB PROAC PTHSS TR2 TUS 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD ABUWG AZQEC C1K DWQXO F28 FR3 H8D H8G JG9 JQ2 KR7 L7M L~C L~D P64 PKEHL PQEST PQQKQ PQUKI PRINS 7S9 L.6 PUEGO
ID	FETCH-LOGICAL-c394t-f8e2b710cb32c2a80cdc005baa9e3be1665f84c264a32c3bfd30541dc5c51fcd3
IEDL.DBID	M7S
ISICitedReferencesCount	28
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000615466000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2072-4292
IngestDate	Mon Nov 17 07:19:38 EST 2025 Thu Sep 04 18:44:41 EDT 2025 Mon Oct 20 02:59:15 EDT 2025 Tue Nov 18 21:47:40 EST 2025 Sat Nov 29 07:19:26 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	3
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c394t-f8e2b710cb32c2a80cdc005baa9e3be1665f84c264a32c3bfd30541dc5c51fcd3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-9539-0050 0000-0001-8419-6511
OpenAccessLink	https://www.proquest.com/docview/2482447819?pq-origsite=%requestingapplication%
PQID	2482447819
PQPubID	2032338
ParticipantIDs	doaj_primary_oai_doaj_org_article_b919260570014d2d8c04c73c846d28cf proquest_miscellaneous_2551964419 proquest_journals_2482447819 crossref_primary_10_3390_rs13030401 crossref_citationtrail_10_3390_rs13030401
PublicationCentury	2000
PublicationDate	2021-01-01
PublicationDateYYYYMMDD	2021-01-01
PublicationDate_xml	– month: 01 year: 2021 text: 2021-01-01 day: 01
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	Remote sensing (Basel, Switzerland)
PublicationYear	2021
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	Jackson (ref_40) 1981; 17 Ma (ref_4) 2010; 102 Long (ref_27) 2009; 8 Yao (ref_52) 2015; 7 Andraski (ref_3) 2000; 29 ref_13 Ziadi (ref_14) 2008; 100 Cilia (ref_12) 2014; 6 ref_55 ref_10 Landis (ref_37) 1977; 33 ref_54 Fitzgerald (ref_48) 2010; 116 Paiao (ref_18) 2020; 112 DeJonge (ref_41) 2015; 156 ref_17 Lemaire (ref_11) 1999; 216 Basso (ref_16) 2009; 4 Alzaben (ref_22) 2019; 111 Diacono (ref_6) 2013; 33 ref_25 Pedregosa (ref_31) 2011; 12 Subedi (ref_2) 2009; 110 ref_24 Rouse (ref_19) 1974; Volume 351 Cao (ref_7) 2017; 214 Zhao (ref_15) 2018; 93 ref_20 Cammarano (ref_44) 2011; 103 Virtanen (ref_30) 2020; 17 Freeman (ref_38) 2007; 99 ref_29 ref_28 Gebbers (ref_1) 2010; 327 Chlingaryan (ref_53) 2018; 151 Jordan (ref_26) 1969; 50 ref_35 Huang (ref_34) 2015; 7 ref_32 Clarke (ref_47) 2001; 3 Fabrizzi (ref_23) 2017; 107 Serrano (ref_21) 2000; 40 ref_39 Jensen (ref_43) 1990; 11 Schratz (ref_33) 2019; 406 Yan (ref_42) 2010; 148 Bushong (ref_49) 2016; 17 Bean (ref_50) 2018; 110 Li (ref_46) 2014; 157 Perry (ref_45) 2012; 134 Cassman (ref_8) 2006; 79 Cohen (ref_36) 1960; 20 Ransom (ref_51) 2019; 164 ref_5 Mistele (ref_9) 2008; 29
References_xml	– ident: ref_32 doi: 10.1145/2939672.2939785 – volume: 3 start-page: 1279 year: 2001 ident: ref_47 article-title: Planar Domain Indices: A Method for Measuring a Quality of a Single Component in Two-Component Pixels publication-title: Int. Geosci. Remote Sens. Symp. – volume: 100 start-page: 1264 year: 2008 ident: ref_14 article-title: Chlorophyll Measurements and Nitrogen Nutrition Index for the Evaluation of Corn Nitrogen Status publication-title: Agron. J. doi: 10.2134/agronj2008.0016 – volume: 112 start-page: 2998 year: 2020 ident: ref_18 article-title: Ground-Based Optical Canopy Sensing Technologies for Corn–Nitrogen Management in the Upper Midwest publication-title: Agron. J. doi: 10.1002/agj2.20248 – volume: 102 start-page: 134 year: 2010 ident: ref_4 article-title: On-Farm Assessment of the Amount and Timing of Nitrogen Fertilizer on Ammonia Volatilization publication-title: Agron. J. doi: 10.2134/agronj2009.0021 – volume: 156 start-page: 51 year: 2015 ident: ref_41 article-title: Comparison of Canopy Temperature-Based Water Stress Indices for Maize publication-title: Agric. Water Manag. doi: 10.1016/j.agwat.2015.03.023 – volume: 111 start-page: 3207 year: 2019 ident: ref_22 article-title: An Inverse Correlation between Corn Temperature and Nitrogen Stress: A Field Case Study publication-title: Agron. J. doi: 10.2134/agronj2019.04.0309 – volume: 20 start-page: 37 year: 1960 ident: ref_36 article-title: A Coefficient of Agreement for Nominal Scales publication-title: Educ. Psychol. Meas. doi: 10.1177/001316446002000104 – volume: 6 start-page: 6549 year: 2014 ident: ref_12 article-title: Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery publication-title: Remote Sens. doi: 10.3390/rs6076549 – volume: 134 start-page: 158 year: 2012 ident: ref_45 article-title: Rapid Estimation of Canopy Nitrogen of Cereal Crops at Paddock Scale Using a Canopy Chlorophyll Content Index publication-title: Field Crops Res. doi: 10.1016/j.fcr.2012.06.003 – volume: 17 start-page: 261 year: 2020 ident: ref_30 article-title: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python publication-title: Nat. Methods doi: 10.1038/s41592-019-0686-2 – volume: 11 start-page: 181 year: 1990 ident: ref_43 article-title: Canopy-Air Temperature of Crops Grown under Different Irrigation Regimes in a Temperate Humid Climate publication-title: Irrig. Sci. doi: 10.1007/BF00189456 – volume: 103 start-page: 1597 year: 2011 ident: ref_44 article-title: Use of the Canopy Chlorophyl Content Index (CCCI) for Remote Estimation of Wheat Nitrogen Content in Rainfed Environments publication-title: Agron. J. doi: 10.2134/agronj2011.0124 – volume: 17 start-page: 470 year: 2016 ident: ref_49 article-title: Evaluation of Mid-Season Sensor Based Nitrogen Fertilizer Recommendations for Winter Wheat Using Different Estimates of Yield Potential publication-title: Precis. Agric. doi: 10.1007/s11119-016-9431-3 – ident: ref_55 doi: 10.3390/rs12020215 – ident: ref_10 – volume: 164 start-page: 104872 year: 2019 ident: ref_51 article-title: Statistical and Machine Learning Methods Evaluated for Incorporating Soil and Weather into Corn Nitrogen Recommendations publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2019.104872 – volume: 110 start-page: 21 year: 2009 ident: ref_2 article-title: Assessment of Some Major Yield-Limiting Factors on Maize Production in a Humid Temperate Environment publication-title: Field Crops Res. doi: 10.1016/j.fcr.2008.06.013 – ident: ref_13 doi: 10.3390/rs8070605 – ident: ref_17 doi: 10.1007/s11119-008-9091-z – volume: 157 start-page: 111 year: 2014 ident: ref_46 article-title: Improving Estimation of Summer Maize Nitrogen Status with Red Edge-Based Spectral Vegetation Indices publication-title: Field Crops Res. doi: 10.1016/j.fcr.2013.12.018 – ident: ref_35 doi: 10.1038/s41598-017-14597-1 – volume: 4 start-page: 147 year: 2009 ident: ref_16 article-title: Criteria for Selecting Optimal Nitrogen Fertilizer Rates for Precision Agriculture publication-title: Ital. J. Agron. doi: 10.4081/ija.2009.4.147 – ident: ref_20 – volume: 93 start-page: 113 year: 2018 ident: ref_15 article-title: Exploring New Spectral Bands and Vegetation Indices for Estimating Nitrogen Nutrition Index of Summer Maize publication-title: Eur. J. Agron. doi: 10.1016/j.eja.2017.12.006 – ident: ref_28 – volume: 33 start-page: 159 year: 1977 ident: ref_37 article-title: The Measurement of Observer Agreement for Categorical Data publication-title: Biometrics doi: 10.2307/2529310 – volume: 327 start-page: 828 year: 2010 ident: ref_1 article-title: Precision Agriculture and Food Security publication-title: Science doi: 10.1126/science.1183899 – ident: ref_24 – volume: 33 start-page: 219 year: 2013 ident: ref_6 article-title: Precision Nitrogen Management of Wheat. A Review publication-title: Agron. Sustain. Dev. doi: 10.1007/s13593-012-0111-z – volume: 107 start-page: 33 year: 2017 ident: ref_23 article-title: Corn and Soybean’s Season-Long in-Situ Nitrogen Mineralization in Drained and Undrained Soils publication-title: Nutr. Cycl. Agroecosyst. doi: 10.1007/s10705-016-9810-1 – volume: 40 start-page: 723 year: 2000 ident: ref_21 article-title: Remote Sensing of Biomass and Yield of Winter Wheat under Different Nitrogen Supplies publication-title: Crop Sci. doi: 10.2135/cropsci2000.403723x – volume: 7 start-page: 14939 year: 2015 ident: ref_52 article-title: Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration publication-title: Remote Sens. doi: 10.3390/rs71114939 – volume: 216 start-page: 65 year: 1999 ident: ref_11 article-title: Relationships between Dynamics of Nitrogen Uptake and Dry Matter Accumulation in Maize Crops. Determination of Critical N Concentration publication-title: Plant Soil doi: 10.1023/A:1004783431055 – volume: 29 start-page: 1095 year: 2000 ident: ref_3 article-title: Crop Management and Corn Nitrogen Rate Effects on Nitrate Leaching publication-title: J. Environ. Qual. doi: 10.2134/jeq2000.00472425002900040009x – volume: 29 start-page: 184 year: 2008 ident: ref_9 article-title: Estimating the Nitrogen Nutrition Index Using Spectral Canopy Reflectance Measurements publication-title: Eur. J. Agron. doi: 10.1016/j.eja.2008.05.007 – volume: 50 start-page: 663 year: 1969 ident: ref_26 article-title: Derivation of Leaf-Area Index from Quality of Light on the Forest Floor publication-title: Ecology doi: 10.2307/1936256 – volume: 406 start-page: 109 year: 2019 ident: ref_33 article-title: Hyperparameter Tuning and Performance Assessment of Statistical and Machine-Learning Algorithms Using Spatial Data publication-title: Ecol. Model. doi: 10.1016/j.ecolmodel.2019.06.002 – ident: ref_39 doi: 10.1109/Agro-Geoinformatics.2016.7577610 – volume: 17 start-page: 1133 year: 1981 ident: ref_40 article-title: Canopy Temperature as a Crop Water Stress Indicator publication-title: Water Resour. Res. doi: 10.1029/WR017i004p01133 – volume: 148 start-page: 329 year: 2010 ident: ref_42 article-title: The Impact of Relative Humidity, Genotypes and Fertilizer Application Rates on Panicle, Leaf Temperature, Fertility and Seed Setting of Rice publication-title: J. Agric. Sci. doi: 10.1017/S0021859610000018 – volume: 214 start-page: 365 year: 2017 ident: ref_7 article-title: Improving Nitrogen Use Efficiency with Minimal Environmental Risks Using an Active Canopy Sensor in a Wheat-Maize Cropping System publication-title: Field Crops Res. doi: 10.1016/j.fcr.2017.09.033 – volume: 8 start-page: 1 year: 2009 ident: ref_27 article-title: Assessing Nitrogen Status of Dryland Wheat Using the Canopy Chlorophyll Content Index publication-title: Crop Manag. doi: 10.1094/CM-2009-1211-01-RS – volume: 110 start-page: 2541 year: 2018 ident: ref_50 article-title: Improving an Active-Optical Reflectance Sensor Algorithm Using Soil and Weather Information publication-title: Agron. J. doi: 10.2134/agronj2017.12.0733 – ident: ref_5 doi: 10.3390/su9081339 – ident: ref_25 – volume: Volume 351 start-page: 309 year: 1974 ident: ref_19 article-title: Monitoring Vegetation Systems in the Great Plains with ERTS publication-title: Proceedings of the Third Earth Resources Technology Satellite—1 Symposium – ident: ref_29 – volume: 12 start-page: 2825 year: 2011 ident: ref_31 article-title: Scikit-Learn: Machine Learning in Python publication-title: J. Mach. Learn. Res. – volume: 7 start-page: 10646 year: 2015 ident: ref_34 article-title: Satellite Remote Sensing-Based in-Season Diagnosis of Rice Nitrogen Status in Northeast China publication-title: Remote Sens. doi: 10.3390/rs70810646 – volume: 79 start-page: 132 year: 2006 ident: ref_8 article-title: Agroecosystems, Nitrogen-Use Efficiency, and Nitrogen Management publication-title: Biogeochemistry – volume: 151 start-page: 61 year: 2018 ident: ref_53 article-title: Machine Learning Approaches for Crop Yield Prediction and Nitrogen Status Estimation in Precision Agriculture: A Review publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.05.012 – ident: ref_54 doi: 10.3390/rs10122026 – volume: 99 start-page: 530 year: 2007 ident: ref_38 article-title: By-Plant Prediction of Corn Forage Biomass and Nitrogen Uptake at Various Growth Stages Using Remote Sensing and Plant Height publication-title: Agron. J. doi: 10.2134/agronj2006.0135 – volume: 116 start-page: 318 year: 2010 ident: ref_48 article-title: Measuring and Predicting Canopy Nitrogen Nutrition in Wheat Using a Spectral Index-The Canopy Chlorophyll Content Index (CCCI) publication-title: Field Crops Res. doi: 10.1016/j.fcr.2010.01.010
SSID	ssj0000331904
Score	2.413198
Snippet	Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy...
SourceID	doaj proquest crossref
SourceType	Open Website Aggregation Database Enrichment Source Index Database
StartPage	401
SubjectTerms	aboveground biomass active canopy sensing Air temperature Atmospheric pressure Biomass Canopies canopy Cell division Chlorophyll Corn Crops developmental stages Diagnosis Drainage Drainage systems experimental design Farms Fertilizers field experimentation Growing season Growth stage Humidity Information management integrated sensing system Leaf area Leaf area index Learning algorithms Machine learning Minnesota model validation Modelling Nitrogen nitrogen content nitrogen nutrition index no-tillage normalized difference vegetation index Normalized difference vegetative index Nutrition Nutrition assessment Parameters Performance prediction phenomics photosynthetically active radiation Plant growth precision nitrogen management reflectance Regression analysis Regression models Remote sensing Seasons Sensors soil Soils strip tillage Tillage Vegetables Vegetation Vegetation index Zea mays
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Na9wwEBVhCaSX0nyUbpIGheSSg4ltySvpmG4aWgjLQhrIzUhjqWto7GDvBvrvM2M724QWcunVHmwxoxm9kUZvGDu1JoDU1kfU1CGSzvtIJzZExFXmrMwS5Tp2_Ws1m-m7OzN_0eqLasJ6euBecefOIAZBzE3no7JICw2xBCUA180i1RAo-sbKvEimuhgscGrFsucjFZjXnzctRWucssmrFagj6v8rDneLy9UH9n5AhfyiH8022_DVDtsaGpQvfu8ymNZNxWflsqnR4pwg4qrll32dXNly2k7ltuLfhyanj553V2ujuaXqK1Qenzb1A5-WDf6Azxe-qu_5DVWvVz95T1u-x26vvv6YfouG_ggRCCOXUdA-dYgQwIkUUqtjKACdyllrvHA-mUyyoCUg5LEoIFwo0LllUkAGWRKgEB_ZqKor_4nxgGlLKpWYmCClsApho3RKuYDfEt6LMTt71lkOA3k49bD4lWMSQfrN_-h3zE7Wsg89ZcY_pb6Q6tcSRHPdPUDj54Px87eMP2aHz4bLB99r81RqxCwKoc6YHa9fo9fQUYitfL1CGQSKhqCg2f8f4zhg71Kqdem2Zg7ZaNms_Ge2CY_Lsm2Ouqn5BCTN5-Y priority: 102 providerName: Directory of Open Access Journals
Title	Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System
URI	https://www.proquest.com/docview/2482447819 https://www.proquest.com/docview/2551964419 https://doaj.org/article/b919260570014d2d8c04c73c846d28cf
Volume	13
WOSCitedRecordID	wos000615466000001&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: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: DOA dateStart: 20090101 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: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: M~E dateStart: 20090101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: P5Z dateStart: 20090301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: PCBAR dateStart: 20090301 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: M7S dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central (subscription) customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: BENPR dateStart: 20090301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 2072-4292 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000331904 issn: 2072-4292 databaseCode: PIMPY dateStart: 20090301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELagRYJLeVZsaVdGcOEQdRM7G-eE6HYrKsEqoiAVLpE9sduVINkmu5V64bcz43i3QiAuXHxIRk6UefjzePINY6917kAqbSNq6hBJY22kYu0i4iozWqZxZjy7_odsNlPn53kREm5dKKtcx0QfqKsGKEd-mEiFK1GGC9jbxVVEXaPodDW00LjLtoklIfale2ebHMtIoIGNZM9KKnB3f9h2FLPRcOPf1iFP1_9HNPZLzMnD_325R2wngEv-rreGx-yOrZ-w-6HP-eXNUwaTpq35bL5sGzQcTkhz1fHjvtxu3nHKynJd89PQK_Xacv-HblRoKuJCHfBJ2yz4ZN7iA3hxaevmBz-jIvj6gvfs58_Yl5Pp58n7KLRZiEDkchk5ZRODQAOMSCDRagQVoG8arXMrjI3H49QpCYicNAoI4yqMETKuIIU0dlCJXbZVN7V9zrjD3U8iMzHOnZRCZ4g-pcky43AuYa0YsDfrj15C4CCnVhjfS9yLkILKWwUN2KuN7KJn3vir1BHpbiNBbNn-QtNelMH5SpMjjsV9G52xyyqpFIwkZAIQe1WJAjdg-2u1lsGFu_JWpwP2cnMbnY9OVHRtmxXKIN7MCVHme_-e4gV7kFAxjM_d7LOtZbuyB-weXC_nXTtk20fTWfFp6BMCQ2_DNP6c4lik3_B-cfqx-PoLD379jg
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFKlceCMCBRYBBw5W7d1NbB9QBSlVo6aRJYpUTmZ3vdtGau1gJ636p_obO-NHKgTi1gNXe7SWvd88dzwfwHsVOyMjZT0idfCkttaLAuU8mlWmlRwEoa6n60_C6TQ6OoqTNbjq_oWhtsrOJtaGOisM1ci3uIzQE4XowLbnvzxijaLT1Y5Co4HFvr28wJSt-jTewf39wPnu18PRnteyCnhGxHLhuchyjX7VaMENV5FvMoNQ1ErFVmgbDIcDF0mDgYJCAaFdhiohg8wMzCBwJhO47h1YlwT2Hqwn44Pkx6qq4wuEtC-bOahCxP5WWZGXQFUJfvN8NUHAH_a_dmq7D_63z_EQ7rfhM_vc4P0RrNn8MWy0TO4nl0_AjIoyZ9PZoixQNRjF0suK7TQNhbOKUd2ZqZyNWzbYc8vqf5C9RFGbGqKMjcpizkazEh_AkhObF2fsG7X558esme_-FL7fyjs-g15e5PY5MIf5HZehGMZOSqFCjK-lDkPtcC1hrejDx26TU9NOWSeyj9MUsy0CRHoDiD68W8nOm9kif5X6QlhZSdA88PpCUR6nrXlJdYyROmam1EUgM55FxpcmFAajy4xHxvVhs4NR2hqpKr3BUB_erm6jeaEzI5XbYokyGFHHFDPHL_69xBvY2Ds8mKST8XT_Jdzj1PpTV6o2obcol_YV3DXni1lVvm51hsHP28blNXgsV6U
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VgoBLeYtAgUXAgYMVe3cd24eqgoSIqFVkCZAqLu7uereNBHawk6L-NX5dZ_xIhUDceuBqj9ay95vnfp4BeK0SZ2SsrEdDHTyprfXiQDmPepVpJcMg0k13_cNoPo-PjpJ0C371_8IQrbK3iY2hzktDNfIhlzF6oggd2NB1tIh0Mt1f_vBoghSdtPbjNFqIHNjzn5i-1XuzCe71G86nHz6PP3rdhAHPiESuPBdbrtHHGi244Sr2TW4QllqpxAptg9EodLE0GDQoFBDa5ageMshNaMLAmVzgutfgeoQ5JtEJ0_Drpr7jCwS3L9uOqEIk_rCqyV-g0gS_-cBmVMAfnqBxb9M7__OHuQs7XVDN3rVacA-2bHEfbnXz3U_PH4AZl1XB5otVVaLCMIqw1zWbtDTDRc2oGs1UwWbdjNgzy5o_k71UEXkNscfGVblk40WFD2DpqS3K7-wTkf-LE9Z2fX8IX67kHR_BdlEW9jEwh1kfl5EYJU5KoSKMuqWOIu1wLWGtGMDbfsMz0_VepxEg3zLMwQgc2SU4BvBqI7tsO478Veo94WYjQV3CmwtldZJ1RifTCcbvmK8St0DmPI-NL00kDMacOY-NG8BuD6msM111domnAbzc3EajQydJqrDlGmUwzk4okk6e_HuJF3ATwZgdzuYHT-E2Jz5QU77ahe1VtbbP4IY5Wy3q6nmjPAyOrxqUF_OQXwg
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=Corn+Nitrogen+Status+Diagnosis+with+an+Innovative+Multi-Parameter+Crop+Circle+Phenom+Sensing+System&rft.jtitle=Remote+sensing+%28Basel%2C+Switzerland%29&rft.date=2021-01-01&rft.pub=MDPI+AG&rft.eissn=2072-4292&rft.volume=13&rft.issue=3&rft.spage=401&rft_id=info:doi/10.3390%2Frs13030401&rft.externalDBID=HAS_PDF_LINK
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2072-4292&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2072-4292&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2072-4292&client=summon