Estimating above ground biomass of winter wheat at early growth stages using digital images and deep convolutional neural network

•A deep convolutional neural network is proposed to the estimate above ground biomass for winter wheat at early growth stages.•The proposed method is using RGB images of winter wheat canopy as input.•The estimated above ground biomass shows a strong correlation to the actual measurements.•The propos...

Full description

Saved in:
Bibliographic Details
Published inEuropean journal of agronomy Vol. 103; pp. 117 - 129
Main Authors Ma, Juncheng, Li, Yunxia, Chen, Yunqiang, Du, Keming, Zheng, Feixiang, Zhang, Lingxian, Sun, Zhongfu
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.02.2019
Subjects
Online AccessGet full text

Cover

Loading…
Abstract •A deep convolutional neural network is proposed to the estimate above ground biomass for winter wheat at early growth stages.•The proposed method is using RGB images of winter wheat canopy as input.•The estimated above ground biomass shows a strong correlation to the actual measurements.•The proposed method has a good potential for the estimation of the growth-related traits. Above ground biomass (AGB) is a critical trait indicating the growth of winter wheat. Currently, non-destructive methods for measuring AGB heavily depend on tools such as Remote Sensing and LiDAR, which is subject to specialized knowledge and high-cost. Low-cost solutions appear therefore to be a necessary supplement. In this study, an easy-to-use AGB estimation method for winter wheat at early growth stages was proposed by using digital images captured under field conditions and Deep Convolutional Neural Network (DCNN). Using canopy images as input, the DCNN was trained to learn the relationship between the canopy and the corresponding AGB. To compare the results of the DCNN, conventionally adopted methods for estimating AGB in conjunction with some color and texture feature extraction techniques were used. Results showed strong correlations could be observed between the actual measurements of AGB to those estimated by the DCNN, with high coefficient of determination (R2 = 0.808) and low Root-Mean-Square-Error (RMSE = 0.8913 kg/plot, NRMSE = 24.95%). Factors may influence the accuracy of the DCNN were evaluated. Results showed selecting suitable values of these factors for the DCNN was the guarantee to accurate estimation results. Plant density was proved to be an influence of factor to all the estimation methods based on digital images. The performances of all the methods were influenced to varying degrees while the DCNN achieved the best robustness, indicating the DCNN with RGB images could be an efficient and robust tool for estimating AGB of winter wheat at early growth stages.
AbstractList •A deep convolutional neural network is proposed to the estimate above ground biomass for winter wheat at early growth stages.•The proposed method is using RGB images of winter wheat canopy as input.•The estimated above ground biomass shows a strong correlation to the actual measurements.•The proposed method has a good potential for the estimation of the growth-related traits. Above ground biomass (AGB) is a critical trait indicating the growth of winter wheat. Currently, non-destructive methods for measuring AGB heavily depend on tools such as Remote Sensing and LiDAR, which is subject to specialized knowledge and high-cost. Low-cost solutions appear therefore to be a necessary supplement. In this study, an easy-to-use AGB estimation method for winter wheat at early growth stages was proposed by using digital images captured under field conditions and Deep Convolutional Neural Network (DCNN). Using canopy images as input, the DCNN was trained to learn the relationship between the canopy and the corresponding AGB. To compare the results of the DCNN, conventionally adopted methods for estimating AGB in conjunction with some color and texture feature extraction techniques were used. Results showed strong correlations could be observed between the actual measurements of AGB to those estimated by the DCNN, with high coefficient of determination (R2 = 0.808) and low Root-Mean-Square-Error (RMSE = 0.8913 kg/plot, NRMSE = 24.95%). Factors may influence the accuracy of the DCNN were evaluated. Results showed selecting suitable values of these factors for the DCNN was the guarantee to accurate estimation results. Plant density was proved to be an influence of factor to all the estimation methods based on digital images. The performances of all the methods were influenced to varying degrees while the DCNN achieved the best robustness, indicating the DCNN with RGB images could be an efficient and robust tool for estimating AGB of winter wheat at early growth stages.
Author Li, Yunxia
Ma, Juncheng
Du, Keming
Zheng, Feixiang
Sun, Zhongfu
Chen, Yunqiang
Zhang, Lingxian
Author_xml – sequence: 1
  givenname: Juncheng
  surname: Ma
  fullname: Ma, Juncheng
  organization: Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
– sequence: 2
  givenname: Yunxia
  surname: Li
  fullname: Li, Yunxia
  organization: College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
– sequence: 3
  givenname: Yunqiang
  surname: Chen
  fullname: Chen, Yunqiang
  organization: College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
– sequence: 4
  givenname: Keming
  orcidid: 0000-0003-1396-9913
  surname: Du
  fullname: Du, Keming
  email: dukeming@caas.cn
  organization: Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
– sequence: 5
  givenname: Feixiang
  orcidid: 0000-0002-5163-8026
  surname: Zheng
  fullname: Zheng, Feixiang
  organization: Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
– sequence: 6
  givenname: Lingxian
  orcidid: 0000-0002-8665-7075
  surname: Zhang
  fullname: Zhang, Lingxian
  organization: College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
– sequence: 7
  givenname: Zhongfu
  surname: Sun
  fullname: Sun, Zhongfu
  organization: Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
BookMark eNp9kN1KxDAQhYOs4O8DeJcXaJ1p2m6LVyL-wYI3eh3SZLqbdU0kye6yl765qXotDMwwM-fjcM7YzHlHjF0hlAjYXq9LWquyAuxKrEqA-oidYjcXxVwInOUZWyxAAJ6wsxjXANBVTX3Kvu5jsh8qWbfkavA74svgt87wwfoPFSP3I99blyjw_YpU4rlIhc1h-tunFY9JLSnybZwIxi5tUhueidNSZY4h-uTau53fbJP1Ll8dbcNPS3sf3i_Y8ag2kS7_-jl7e7h_vXsqFi-Pz3e3i0KLVqSi7huldd_W0CE2UDfd0GOvW9MMowDVNQpEPzYaO9EYI0RNMNCgjdDtvIWKxDnDX64OPsZAo_wM2Wc4SAQ5ZSjXMmcopwwlVjJnmDU3vxrKxnaWgozaktNkbCCdpPH2H_U3Uyl-WA
CitedBy_id crossref_primary_10_1016_j_compag_2023_107957
crossref_primary_10_3390_rs12010017
crossref_primary_10_17221_91_2021_CJGPB
crossref_primary_10_3390_rs15235444
crossref_primary_10_1080_1343943X_2023_2210767
crossref_primary_10_3390_fi16050145
crossref_primary_10_3390_rs13163095
crossref_primary_10_1016_j_isprsjprs_2020_12_010
crossref_primary_10_1016_j_compag_2019_105159
crossref_primary_10_1007_s10722_019_00816_3
crossref_primary_10_1016_j_compag_2024_108642
crossref_primary_10_1016_j_rse_2022_113262
crossref_primary_10_3390_rs14215333
crossref_primary_10_1007_s11119_020_09734_2
crossref_primary_10_3390_s24082464
crossref_primary_10_1080_07038992_2021_1926952
crossref_primary_10_1016_j_ecolind_2024_112061
crossref_primary_10_3390_rs14133052
crossref_primary_10_1016_j_biosystemseng_2021_09_015
crossref_primary_10_3390_drones6120423
crossref_primary_10_3390_agronomy12040807
crossref_primary_10_1016_j_asoc_2022_109761
crossref_primary_10_1007_s11042_022_12160_3
crossref_primary_10_1016_j_watres_2023_120024
crossref_primary_10_1080_01431161_2022_2026521
crossref_primary_10_3390_rs14163912
crossref_primary_10_3389_fpls_2023_1204791
crossref_primary_10_3390_agriculture14030378
crossref_primary_10_1016_j_compag_2021_106480
crossref_primary_10_3390_agronomy12061352
crossref_primary_10_1007_s42452_020_03599_w
crossref_primary_10_34133_plantphenomics_0073
crossref_primary_10_1007_s12230_022_09897_w
crossref_primary_10_1016_j_compag_2020_105519
crossref_primary_10_3390_rs14112692
crossref_primary_10_1016_j_eswa_2021_116226
crossref_primary_10_3390_s20174802
crossref_primary_10_1016_j_biosystemseng_2023_08_002
crossref_primary_10_1016_j_agrformet_2020_107938
crossref_primary_10_1016_S2095_3119_20_63319_6
crossref_primary_10_3390_agriculture12030384
crossref_primary_10_1016_j_agwat_2023_108140
crossref_primary_10_3389_fpls_2024_1367828
crossref_primary_10_3390_agronomy10020175
crossref_primary_10_1016_j_compag_2020_105662
crossref_primary_10_1080_01431161_2022_2107882
crossref_primary_10_3390_rs11222678
crossref_primary_10_1016_j_compag_2022_107087
crossref_primary_10_1038_s41438_020_00345_6
crossref_primary_10_1080_1343943X_2022_2103003
crossref_primary_10_1016_j_compag_2021_106092
crossref_primary_10_34133_2022_9850486
crossref_primary_10_3390_su16031051
crossref_primary_10_3390_rs14122864
crossref_primary_10_1016_j_cej_2023_147174
crossref_primary_10_1016_j_compag_2020_105786
crossref_primary_10_3390_s21123971
crossref_primary_10_1016_j_eja_2020_126030
crossref_primary_10_1016_j_fcr_2022_108582
crossref_primary_10_1007_s11119_022_09932_0
Cites_doi 10.1016/j.biombioe.2017.06.027
10.1016/j.compag.2017.05.032
10.3390/rs8090706
10.3390/rs9010098
10.3389/fpls.2015.00619
10.1016/j.compag.2016.09.017
10.1016/j.jag.2015.02.012
10.1016/j.compag.2017.10.027
10.1016/j.eja.2016.04.013
10.1016/j.rse.2015.02.023
10.1016/j.compag.2016.02.003
10.1016/j.compag.2016.01.007
10.1016/j.fcr.2017.11.024
10.2134/agronj15.0150
10.1016/j.eswa.2018.01.055
10.1016/j.compag.2016.08.021
10.1016/j.fcr.2016.06.022
10.1186/s13007-015-0048-8
10.1016/j.compag.2018.01.009
10.1016/j.compag.2017.08.023
10.1364/AO.16.001151
10.13031/2013.27838
10.1016/j.compag.2018.08.048
10.1186/s13007-017-0254-7
10.1016/j.fcr.2014.01.008
10.1016/j.biosystemseng.2017.02.004
10.1016/S0034-4257(00)00113-9
10.1038/nature14539
10.3390/s150202920
10.1016/0034-4257(79)90013-0
10.1016/j.biosystemseng.2014.11.007
10.1016/j.cj.2016.01.008
10.1111/jipb.12117
10.1016/j.compag.2016.07.003
10.14358/PERS.73.10.1141
10.1016/j.eja.2015.11.026
10.1016/j.jag.2014.05.006
10.1016/j.isprsjprs.2017.01.010
10.1023/A:1010933404324
ContentType Journal Article
Copyright 2018 Elsevier B.V.
Copyright_xml – notice: 2018 Elsevier B.V.
DBID AAYXX
CITATION
DOI 10.1016/j.eja.2018.12.004
DatabaseName CrossRef
DatabaseTitle CrossRef
DatabaseTitleList
DeliveryMethod fulltext_linktorsrc
Discipline Agriculture
EISSN 1873-7331
EndPage 129
ExternalDocumentID 10_1016_j_eja_2018_12_004
S1161030118301977
GroupedDBID --K
--M
.~1
0R~
1B1
1RT
1~.
1~5
29G
4.4
457
4G.
5GY
5VS
7-5
71M
8P~
AABVA
AACTN
AAEDT
AAEDW
AAIAV
AAIKJ
AAKOC
AALCJ
AALRI
AAOAW
AAQFI
AAQXK
AATLK
AAXUO
ABFNM
ABGRD
ABJNI
ABMAC
ABXDB
ABYKQ
ACDAQ
ACGFS
ACIUM
ACRLP
ADBBV
ADEZE
ADMUD
ADQTV
AEBSH
AEKER
AENEX
AEQOU
AFKWA
AFTJW
AFXIZ
AGHFR
AGUBO
AGYEJ
AIEXJ
AIKHN
AITUG
AJBFU
AJOXV
ALMA_UNASSIGNED_HOLDINGS
AMFUW
AMRAJ
ASPBG
AVWKF
AXJTR
AZFZN
BKOJK
BLXMC
CBWCG
CS3
DU5
EBS
EFJIC
EFLBG
EJD
EO8
EO9
EP2
EP3
FDB
FEDTE
FGOYB
FIRID
FNPLU
FYGXN
G-Q
GBLVA
HVGLF
HZ~
IHE
J1W
KOM
M41
MO0
N9A
O-L
O9-
OAUVE
OZT
P-8
P-9
P2P
PC.
Q38
R2-
RIG
ROL
RPZ
SDF
SDG
SES
SEW
SPCBC
SSA
SSZ
T5K
UHS
~G-
~KM
AAHBH
AAXKI
AAYXX
ACRPL
ADNMO
AFJKZ
AKRWK
CITATION
ID FETCH-LOGICAL-c363t-495acc964081150458b919c6d5bf30a85a039f5c1835dd334e0bebcd3c67602e3
IEDL.DBID .~1
ISSN 1161-0301
IngestDate Fri Dec 06 02:53:52 EST 2024
Fri Feb 23 02:28:06 EST 2024
IsPeerReviewed true
IsScholarly true
Keywords Above ground biomass
RGB images
Deep convolutional neural network
Winter wheat
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c363t-495acc964081150458b919c6d5bf30a85a039f5c1835dd334e0bebcd3c67602e3
ORCID 0000-0003-1396-9913
0000-0002-8665-7075
0000-0002-5163-8026
PageCount 13
ParticipantIDs crossref_primary_10_1016_j_eja_2018_12_004
elsevier_sciencedirect_doi_10_1016_j_eja_2018_12_004
PublicationCentury 2000
PublicationDate February 2019
2019-02-00
PublicationDateYYYYMMDD 2019-02-01
PublicationDate_xml – month: 02
  year: 2019
  text: February 2019
PublicationDecade 2010
PublicationTitle European journal of agronomy
PublicationYear 2019
Publisher Elsevier B.V
Publisher_xml – name: Elsevier B.V
References Breiman (bib0020) 2001; 45
Rahaman, Chen, Gillani, Klukas, Chen (bib0175) 2015; 6
Woebbecke, Meyer, Von Bargen, Mortensen (bib0225) 1995; 38
Ding, Taylor (bib0050) 2016; 123
Simonyan, Zisserman (bib0200) 2014
Tucker (bib0210) 1979; 8
Ma, Du, Zhang, Zheng, Chu, Sun (bib0130) 2017; 142
Krause, Sugita, Baek, Lim (bib0110) 2018
Baresel, Rischbeck, Hu, Kipp, Hu, Barmeier, Mistele (bib0010) 2017; 140
Eitel, Magney, Vierling, Brown, Huggins (bib0055) 2014; 159
LeCun, Bengio, Hinton (bib0120) 2015; 521
Jiang, Li, Paterson (bib0105) 2016; 130
Xiong, Duan, Liu, Tu, Yang, Wu, Chen, Xiong, Yang, Liu (bib0230) 2017; 13
Greaves, Vierling, Eitel, Boelman, Magney, Prager, Griffin (bib0090) 2015; 164
Moeckel, Safari, Reddersen, Fricke, Wachendorf (bib0140) 2017; 9
Mohanty, Hughes, Salathe (bib0145) 2016
Patrignani, Ochsner (bib0155) 2015; 107
Chung, Choi, Silva, Kang, Eom, Kim (bib0035) 2017; 105
Jannoura, Brinkmann, Uteau, Bruns, Joergensen (bib0100) 2015; 129
Schirrmann, Hamdorf, Garz, Ustyuzhanin, Dammer (bib0195) 2016; 121
Krizhevsky, Sutskever, Hinton (bib0115) 2012
Rischbeck, Elsayed, Mistele, Barmeier, Heil, Schmidhalter (bib0185) 2016; 78
Chang, Zaman, Rehman, Farooque, Esau, Jameel (bib0030) 2017; 157
Daughtry, Walthall, Kim, de Colstoun, McMurtrey (bib0045) 2000; 74
Rasmussen, Ntakos, Nielsen, Svensgaard, Poulsen, Christensen (bib0180) 2016; 74
Ferentinos (bib0065) 2018; 145
Gnyp, Bareth, Li, Lenz-Wiedemann, Koppe, Miao, Hennig, Jia, Laudien, Chen, Zhang (bib0085) 2014
Ma, Du, Zheng, Zhang, Gong, Sun (bib0135) 2018; 154
Naito, Ogawa, Valencia, Mohri, Urano, Hosoi, Shimizu, Chavez, Ishitani, Selvaraj, Omasa (bib0150) 2017; 125
Grinblat, Uzal, Larese, Granitto (bib0095) 2016; 127
Wang, Zhou, Zhu, Dong, Guo (bib0220) 2016; 4
Ferreira, Freitas, Silva, Pistori, Folhes (bib0070) 2017; 143
Pölönen, Saari, Kaivosoja, Honkavaara, Pesonen (bib0165) 2013; 888
Fan, Kawamura, Guo, Xuan, Lim, Yuba, Kurokawa, Obitsu, Lv, Tsumiyama, Yasuda, Wang (bib0060) 2017
Gizaw, Garland-Campbell, Carter (bib0080) 2016; 196
Walter, Edwards, McDonald, Kuchel (bib0215) 2018; 216
Tucker (bib0205) 1977; 16
Casadesús, Villegas (bib0025) 2014; 56
Zhang, Verma, Stockwell, Chowdhury (bib0235) 2018; 101
Bai, Ge, Hussain, Baenziger, Graef (bib0005) 2016; 128
Bendig, Yu, Aasen, Bolten, Bennertz, Broscheit, Gnyp, Bareth (bib0015) 2015
Liebisch, Kirchgessner, Schneider, Walter, Hund (bib0125) 2015; 11
Schirrmann, Giebel, Gleiniger, Pflanz, Lentschke, Dammer (bib0190) 2016; 8
Pittman, Arnall, Interrante, Moffet, Butler (bib0160) 2015; 15
Possoch, Bieker, Hoffmeister, Bolten, Schellberg, Bareth, Conservation, Group, Conservation, Ecology, Model (bib0170) 2016; XLI
Clevers, van der Heijden, Verzakov, Schaepman (bib0040) 2007; 73
Ghosal, Blystone, Singh, Ganapathysubramanian, Singh (bib0075) 2018
Rasmussen (10.1016/j.eja.2018.12.004_bib0180) 2016; 74
Bendig (10.1016/j.eja.2018.12.004_bib0015) 2015
Grinblat (10.1016/j.eja.2018.12.004_bib0095) 2016; 127
Ferentinos (10.1016/j.eja.2018.12.004_bib0065) 2018; 145
Jiang (10.1016/j.eja.2018.12.004_bib0105) 2016; 130
Ghosal (10.1016/j.eja.2018.12.004_bib0075) 2018
Krause (10.1016/j.eja.2018.12.004_bib0110) 2018
Greaves (10.1016/j.eja.2018.12.004_bib0090) 2015; 164
Ma (10.1016/j.eja.2018.12.004_bib0135) 2018; 154
Rahaman (10.1016/j.eja.2018.12.004_bib0175) 2015; 6
Tucker (10.1016/j.eja.2018.12.004_bib0210) 1979; 8
Chang (10.1016/j.eja.2018.12.004_bib0030) 2017; 157
Jannoura (10.1016/j.eja.2018.12.004_bib0100) 2015; 129
Casadesús (10.1016/j.eja.2018.12.004_bib0025) 2014; 56
Gnyp (10.1016/j.eja.2018.12.004_bib0085) 2014
Ferreira (10.1016/j.eja.2018.12.004_bib0070) 2017; 143
Woebbecke (10.1016/j.eja.2018.12.004_bib0225) 1995; 38
Pölönen (10.1016/j.eja.2018.12.004_bib0165) 2013; 888
Daughtry (10.1016/j.eja.2018.12.004_bib0045) 2000; 74
Eitel (10.1016/j.eja.2018.12.004_bib0055) 2014; 159
Bai (10.1016/j.eja.2018.12.004_bib0005) 2016; 128
Breiman (10.1016/j.eja.2018.12.004_bib0020) 2001; 45
Schirrmann (10.1016/j.eja.2018.12.004_bib0190) 2016; 8
Krizhevsky (10.1016/j.eja.2018.12.004_bib0115) 2012
Liebisch (10.1016/j.eja.2018.12.004_bib0125) 2015; 11
Ding (10.1016/j.eja.2018.12.004_bib0050) 2016; 123
Mohanty (10.1016/j.eja.2018.12.004_bib0145) 2016
Rischbeck (10.1016/j.eja.2018.12.004_bib0185) 2016; 78
Ma (10.1016/j.eja.2018.12.004_bib0130) 2017; 142
Xiong (10.1016/j.eja.2018.12.004_bib0230) 2017; 13
Chung (10.1016/j.eja.2018.12.004_bib0035) 2017; 105
Walter (10.1016/j.eja.2018.12.004_bib0215) 2018; 216
Wang (10.1016/j.eja.2018.12.004_bib0220) 2016; 4
LeCun (10.1016/j.eja.2018.12.004_bib0120) 2015; 521
Pittman (10.1016/j.eja.2018.12.004_bib0160) 2015; 15
Tucker (10.1016/j.eja.2018.12.004_bib0205) 1977; 16
Gizaw (10.1016/j.eja.2018.12.004_bib0080) 2016; 196
Possoch (10.1016/j.eja.2018.12.004_bib0170) 2016; XLI
Baresel (10.1016/j.eja.2018.12.004_bib0010) 2017; 140
Naito (10.1016/j.eja.2018.12.004_bib0150) 2017; 125
Zhang (10.1016/j.eja.2018.12.004_bib0235) 2018; 101
Moeckel (10.1016/j.eja.2018.12.004_bib0140) 2017; 9
Simonyan (10.1016/j.eja.2018.12.004_bib0200) 2014
Patrignani (10.1016/j.eja.2018.12.004_bib0155) 2015; 107
Clevers (10.1016/j.eja.2018.12.004_bib0040) 2007; 73
Schirrmann (10.1016/j.eja.2018.12.004_bib0195) 2016; 121
Fan (10.1016/j.eja.2018.12.004_bib0060) 2017
References_xml – volume: 128
  start-page: 181
  year: 2016
  end-page: 192
  ident: bib0005
  article-title: A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding
  publication-title: Comput. Electron. Agric.
  contributor:
    fullname: Graef
– volume: 142
  start-page: 110
  year: 2017
  end-page: 117
  ident: bib0130
  article-title: A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing
  publication-title: Comput. Electron. Agric.
  contributor:
    fullname: Sun
– volume: 196
  start-page: 199
  year: 2016
  end-page: 206
  ident: bib0080
  article-title: Use of spectral reflectance for indirect selection of yield potential and stability in Pacific Northwest winter wheat
  publication-title: Field Crop. Res.
  contributor:
    fullname: Carter
– volume: 164
  start-page: 26
  year: 2015
  end-page: 35
  ident: bib0090
  article-title: Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR
  publication-title: Remote Sens. Environ.
  contributor:
    fullname: Griffin
– volume: 121
  start-page: 374
  year: 2016
  end-page: 384
  ident: bib0195
  article-title: Estimating wheat biomass by combining image clustering with crop height
  publication-title: Comput. Electron. Agric.
  contributor:
    fullname: Dammer
– volume: 4
  start-page: 212
  year: 2016
  end-page: 219
  ident: bib0220
  article-title: Estimation of biomass in wheat using random forest regression algorithm and remote sensing data
  publication-title: Crop J.
  contributor:
    fullname: Guo
– volume: 157
  start-page: 35
  year: 2017
  end-page: 44
  ident: bib0030
  article-title: A real-time ultrasonic system to measure wild blueberry plant height during harvesting
  publication-title: Biosyst. Eng.
  contributor:
    fullname: Jameel
– start-page: 1
  year: 2016
  end-page: 6
  ident: bib0145
  article-title: Inference of Plant Diseases from Leaf Images Through Deep Learning
  contributor:
    fullname: Salathe
– volume: 11
  year: 2015
  ident: bib0125
  article-title: Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach
  publication-title: Plant Methods
  contributor:
    fullname: Hund
– volume: 521
  start-page: 436
  year: 2015
  end-page: 444
  ident: bib0120
  article-title: Deep learning
  publication-title: Nature
  contributor:
    fullname: Hinton
– volume: 8
  start-page: 706
  year: 2016
  ident: bib0190
  article-title: Monitoring agronomic parameters of winter wheat crops with low-cost UAV imagery
  publication-title: Remote Sens.
  contributor:
    fullname: Dammer
– volume: 13
  start-page: 1
  year: 2017
  end-page: 15
  ident: bib0230
  article-title: Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization
  publication-title: Plant Methods
  contributor:
    fullname: Liu
– volume: 78
  start-page: 44
  year: 2016
  end-page: 59
  ident: bib0185
  article-title: Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley
  publication-title: Eur. J. Agron.
  contributor:
    fullname: Schmidhalter
– volume: 15
  start-page: 2920
  year: 2015
  end-page: 2943
  ident: bib0160
  article-title: Estimation of biomass and canopy height in Bermudagrass, Alfalfa, and wheat using ultrasonic, laser, and spectral sensors
  publication-title: Sensors (Switzerland)
  contributor:
    fullname: Butler
– volume: 130
  start-page: 57
  year: 2016
  end-page: 68
  ident: bib0105
  article-title: High throughput phenotyping of cotton plant height using depth images under field conditions
  publication-title: Comput. Electron. Agric.
  contributor:
    fullname: Paterson
– volume: XLI
  start-page: 12
  year: 2016
  end-page: 19
  ident: bib0170
  article-title: Multi-temporal crop surface models combined with the RGB vegetation index fromUAV-based images for forage monitoring in grassland
  publication-title: ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
  contributor:
    fullname: Model
– volume: 45
  start-page: 5
  year: 2001
  end-page: 32
  ident: bib0020
  article-title: Random forests
  publication-title: Mach. Learn.
  contributor:
    fullname: Breiman
– volume: 8
  start-page: 127
  year: 1979
  end-page: 150
  ident: bib0210
  article-title: Red and photographic infrared linear combinations for monitoring vegetation
  publication-title: Remote Sens. Environ.
  contributor:
    fullname: Tucker
– volume: 107
  start-page: 2312
  year: 2015
  end-page: 2320
  ident: bib0155
  article-title: Canopeo: a powerful new tool for measuring fractional green canopy cover
  publication-title: Agron. J.
  contributor:
    fullname: Ochsner
– volume: 38
  start-page: 259
  year: 1995
  end-page: 269
  ident: bib0225
  article-title: Color indices for weed identification under various soil, residue, and lighting conditions
  publication-title: Trans. Am. Soc. Agric. Eng.
  contributor:
    fullname: Mortensen
– volume: 9
  start-page: 1
  year: 2017
  end-page: 14
  ident: bib0140
  article-title: Fusion of ultrasonic and spectral sensor data for improving the estimation of biomass in grasslands with heterogeneous sward structure
  publication-title: Remote Sens.
  contributor:
    fullname: Wachendorf
– volume: 73
  start-page: 1141
  year: 2007
  end-page: 1148
  ident: bib0040
  article-title: Estimating grassland biomass using svm band shaving of hyperspectral data
  publication-title: Photogramm. Eng. Rem. Sens.
  contributor:
    fullname: Schaepman
– year: 2014
  ident: bib0085
  article-title: Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain
  publication-title: Int. J. Appl. Earth Obs. Geoinf.
  contributor:
    fullname: Zhang
– start-page: 1
  year: 2017
  end-page: 10
  ident: bib0060
  article-title: A simple visible and near-infrared (V-NIR) camera system for monitoring the leaf area index and growth stage of Italian ryegrass
  publication-title: Comput. Electron. Agric.
  contributor:
    fullname: Wang
– volume: 125
  start-page: 50
  year: 2017
  end-page: 62
  ident: bib0150
  article-title: Estimating rice yield related traits and quantitative trait loci analysis under different nitrogen treatments using a simple tower-based field phenotyping system with modified single-lens reflex cameras
  publication-title: ISPRS J. Photogramm. Remote Sens.
  contributor:
    fullname: Omasa
– volume: 123
  start-page: 17
  year: 2016
  end-page: 28
  ident: bib0050
  article-title: Automatic moth detection from trap images for pest management
  publication-title: Comput. Electron. Agric.
  contributor:
    fullname: Taylor
– start-page: 517
  year: 2018
  end-page: 520
  ident: bib0110
  article-title: WTPlant (what’s that plant?): A deep learning system for identifying plants in natural images
  publication-title: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval - ICMR’ 18
  contributor:
    fullname: Lim
– volume: 16
  start-page: 1151
  year: 1977
  end-page: 1156
  ident: bib0205
  article-title: Asymptotic nature of grass canopy spectral reflectance
  publication-title: Appl. Opt.
  contributor:
    fullname: Tucker
– volume: 159
  start-page: 21
  year: 2014
  end-page: 32
  ident: bib0055
  article-title: LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status
  publication-title: Field Crop. Res.
  contributor:
    fullname: Huggins
– year: 2014
  ident: bib0200
  article-title: Very Deep Convolutional Networks for Large-Scale Image Recognition
  contributor:
    fullname: Zisserman
– volume: 143
  start-page: 314
  year: 2017
  end-page: 324
  ident: bib0070
  article-title: Weed detection in soybean crops using convnets
  publication-title: Comput. Electron. Agric.
  contributor:
    fullname: Folhes
– volume: 6
  start-page: 1
  year: 2015
  end-page: 15
  ident: bib0175
  article-title: Advanced phenotyping and phenotype data analysis for the study of plant growth and development
  publication-title: Front. Plant Sci.
  contributor:
    fullname: Chen
– start-page: 1
  year: 2012
  end-page: 9
  ident: bib0115
  article-title: ImageNet classification with deep convolutional neural networks
  publication-title: Adv. Neural Inf. Process. Syst.
  contributor:
    fullname: Hinton
– volume: 140
  start-page: 25
  year: 2017
  end-page: 33
  ident: bib0010
  article-title: Use of a digital camera as alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat
  publication-title: Comput. Electron. Agric.
  contributor:
    fullname: Mistele
– volume: 129
  start-page: 341
  year: 2015
  end-page: 351
  ident: bib0100
  article-title: Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter
  publication-title: Biosyst. Eng.
  contributor:
    fullname: Joergensen
– volume: 56
  start-page: 7
  year: 2014
  end-page: 14
  ident: bib0025
  article-title: Conventional digital cameras as a tool for assessing leaf area index and biomass for cereal breeding
  publication-title: J. Integr. Plant Biol.
  contributor:
    fullname: Villegas
– volume: 74
  start-page: 229
  year: 2000
  end-page: 239
  ident: bib0045
  article-title: Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance
  publication-title: Remote Sens. Environ.
  contributor:
    fullname: McMurtrey
– volume: 154
  start-page: 18
  year: 2018
  end-page: 24
  ident: bib0135
  article-title: A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network
  publication-title: Comput. Electron. Agric.
  contributor:
    fullname: Sun
– volume: 888
  start-page: 521
  year: 2013
  end-page: 525
  ident: bib0165
  article-title: Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV
  publication-title: Proc. SPIE Int. Soc. Opt. Eng.
  contributor:
    fullname: Pesonen
– volume: 145
  start-page: 311
  year: 2018
  end-page: 318
  ident: bib0065
  article-title: Deep learning models for plant disease detection and diagnosis
  publication-title: Comput. Electron. Agric.
  contributor:
    fullname: Ferentinos
– volume: 216
  start-page: 165
  year: 2018
  end-page: 174
  ident: bib0215
  article-title: Photogrammetry for the estimation of wheat biomass and harvest index
  publication-title: Field Crop. Res.
  contributor:
    fullname: Kuchel
– volume: 74
  start-page: 75
  year: 2016
  end-page: 92
  ident: bib0180
  article-title: Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?
  publication-title: Eur. J. Agron.
  contributor:
    fullname: Christensen
– volume: 105
  start-page: 207
  year: 2017
  end-page: 210
  ident: bib0035
  article-title: Case study: estimation of sorghum biomass using digital image analysis with Canopeo
  publication-title: Biomass Bioenergy
  contributor:
    fullname: Kim
– volume: 127
  start-page: 418
  year: 2016
  end-page: 424
  ident: bib0095
  article-title: Deep learning for plant identification using vein morphological patterns
  publication-title: Comput. Electron. Agric.
  contributor:
    fullname: Granitto
– start-page: 1
  year: 2018
  end-page: 6
  ident: bib0075
  article-title: An explainable deep machine vision framework for plant stress phenotyping
  publication-title: Proc. Natl. Acad. Sci. U. S. A.
  contributor:
    fullname: Singh
– year: 2015
  ident: bib0015
  article-title: Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley
  publication-title: Int. J. Appl. Earth Obs. Geoinf.
  contributor:
    fullname: Bareth
– volume: 101
  start-page: 213
  year: 2018
  end-page: 227
  ident: bib0235
  article-title: Density weighted connectivity of grass pixels in image frames for biomass estimation
  publication-title: Expert Syst. Appl.
  contributor:
    fullname: Chowdhury
– volume: 105
  start-page: 207
  year: 2017
  ident: 10.1016/j.eja.2018.12.004_bib0035
  article-title: Case study: estimation of sorghum biomass using digital image analysis with Canopeo
  publication-title: Biomass Bioenergy
  doi: 10.1016/j.biombioe.2017.06.027
  contributor:
    fullname: Chung
– start-page: 1
  year: 2016
  ident: 10.1016/j.eja.2018.12.004_bib0145
  contributor:
    fullname: Mohanty
– volume: 140
  start-page: 25
  year: 2017
  ident: 10.1016/j.eja.2018.12.004_bib0010
  article-title: Use of a digital camera as alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat
  publication-title: Comput. Electron. Agric.
  doi: 10.1016/j.compag.2017.05.032
  contributor:
    fullname: Baresel
– volume: 888
  start-page: 521
  year: 2013
  ident: 10.1016/j.eja.2018.12.004_bib0165
  article-title: Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV
  publication-title: Proc. SPIE Int. Soc. Opt. Eng.
  contributor:
    fullname: Pölönen
– volume: 8
  start-page: 706
  year: 2016
  ident: 10.1016/j.eja.2018.12.004_bib0190
  article-title: Monitoring agronomic parameters of winter wheat crops with low-cost UAV imagery
  publication-title: Remote Sens.
  doi: 10.3390/rs8090706
  contributor:
    fullname: Schirrmann
– volume: 9
  start-page: 1
  year: 2017
  ident: 10.1016/j.eja.2018.12.004_bib0140
  article-title: Fusion of ultrasonic and spectral sensor data for improving the estimation of biomass in grasslands with heterogeneous sward structure
  publication-title: Remote Sens.
  doi: 10.3390/rs9010098
  contributor:
    fullname: Moeckel
– volume: 6
  start-page: 1
  year: 2015
  ident: 10.1016/j.eja.2018.12.004_bib0175
  article-title: Advanced phenotyping and phenotype data analysis for the study of plant growth and development
  publication-title: Front. Plant Sci.
  doi: 10.3389/fpls.2015.00619
  contributor:
    fullname: Rahaman
– volume: 130
  start-page: 57
  year: 2016
  ident: 10.1016/j.eja.2018.12.004_bib0105
  article-title: High throughput phenotyping of cotton plant height using depth images under field conditions
  publication-title: Comput. Electron. Agric.
  doi: 10.1016/j.compag.2016.09.017
  contributor:
    fullname: Jiang
– year: 2015
  ident: 10.1016/j.eja.2018.12.004_bib0015
  article-title: Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley
  publication-title: Int. J. Appl. Earth Obs. Geoinf.
  doi: 10.1016/j.jag.2015.02.012
  contributor:
    fullname: Bendig
– volume: 143
  start-page: 314
  year: 2017
  ident: 10.1016/j.eja.2018.12.004_bib0070
  article-title: Weed detection in soybean crops using convnets
  publication-title: Comput. Electron. Agric.
  doi: 10.1016/j.compag.2017.10.027
  contributor:
    fullname: Ferreira
– volume: 78
  start-page: 44
  year: 2016
  ident: 10.1016/j.eja.2018.12.004_bib0185
  article-title: Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley
  publication-title: Eur. J. Agron.
  doi: 10.1016/j.eja.2016.04.013
  contributor:
    fullname: Rischbeck
– volume: 164
  start-page: 26
  year: 2015
  ident: 10.1016/j.eja.2018.12.004_bib0090
  article-title: Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR
  publication-title: Remote Sens. Environ.
  doi: 10.1016/j.rse.2015.02.023
  contributor:
    fullname: Greaves
– volume: XLI
  start-page: 12
  year: 2016
  ident: 10.1016/j.eja.2018.12.004_bib0170
  article-title: Multi-temporal crop surface models combined with the RGB vegetation index fromUAV-based images for forage monitoring in grassland
  publication-title: ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
  contributor:
    fullname: Possoch
– volume: 123
  start-page: 17
  year: 2016
  ident: 10.1016/j.eja.2018.12.004_bib0050
  article-title: Automatic moth detection from trap images for pest management
  publication-title: Comput. Electron. Agric.
  doi: 10.1016/j.compag.2016.02.003
  contributor:
    fullname: Ding
– volume: 121
  start-page: 374
  year: 2016
  ident: 10.1016/j.eja.2018.12.004_bib0195
  article-title: Estimating wheat biomass by combining image clustering with crop height
  publication-title: Comput. Electron. Agric.
  doi: 10.1016/j.compag.2016.01.007
  contributor:
    fullname: Schirrmann
– volume: 216
  start-page: 165
  year: 2018
  ident: 10.1016/j.eja.2018.12.004_bib0215
  article-title: Photogrammetry for the estimation of wheat biomass and harvest index
  publication-title: Field Crop. Res.
  doi: 10.1016/j.fcr.2017.11.024
  contributor:
    fullname: Walter
– volume: 107
  start-page: 2312
  year: 2015
  ident: 10.1016/j.eja.2018.12.004_bib0155
  article-title: Canopeo: a powerful new tool for measuring fractional green canopy cover
  publication-title: Agron. J.
  doi: 10.2134/agronj15.0150
  contributor:
    fullname: Patrignani
– volume: 101
  start-page: 213
  year: 2018
  ident: 10.1016/j.eja.2018.12.004_bib0235
  article-title: Density weighted connectivity of grass pixels in image frames for biomass estimation
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2018.01.055
  contributor:
    fullname: Zhang
– volume: 128
  start-page: 181
  year: 2016
  ident: 10.1016/j.eja.2018.12.004_bib0005
  article-title: A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding
  publication-title: Comput. Electron. Agric.
  doi: 10.1016/j.compag.2016.08.021
  contributor:
    fullname: Bai
– volume: 196
  start-page: 199
  year: 2016
  ident: 10.1016/j.eja.2018.12.004_bib0080
  article-title: Use of spectral reflectance for indirect selection of yield potential and stability in Pacific Northwest winter wheat
  publication-title: Field Crop. Res.
  doi: 10.1016/j.fcr.2016.06.022
  contributor:
    fullname: Gizaw
– volume: 11
  year: 2015
  ident: 10.1016/j.eja.2018.12.004_bib0125
  article-title: Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach
  publication-title: Plant Methods
  doi: 10.1186/s13007-015-0048-8
  contributor:
    fullname: Liebisch
– volume: 145
  start-page: 311
  year: 2018
  ident: 10.1016/j.eja.2018.12.004_bib0065
  article-title: Deep learning models for plant disease detection and diagnosis
  publication-title: Comput. Electron. Agric.
  doi: 10.1016/j.compag.2018.01.009
  contributor:
    fullname: Ferentinos
– volume: 142
  start-page: 110
  year: 2017
  ident: 10.1016/j.eja.2018.12.004_bib0130
  article-title: A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing
  publication-title: Comput. Electron. Agric.
  doi: 10.1016/j.compag.2017.08.023
  contributor:
    fullname: Ma
– start-page: 517
  year: 2018
  ident: 10.1016/j.eja.2018.12.004_bib0110
  article-title: WTPlant (what’s that plant?): A deep learning system for identifying plants in natural images
  contributor:
    fullname: Krause
– start-page: 1
  year: 2012
  ident: 10.1016/j.eja.2018.12.004_bib0115
  article-title: ImageNet classification with deep convolutional neural networks
  publication-title: Adv. Neural Inf. Process. Syst.
  contributor:
    fullname: Krizhevsky
– volume: 16
  start-page: 1151
  year: 1977
  ident: 10.1016/j.eja.2018.12.004_bib0205
  article-title: Asymptotic nature of grass canopy spectral reflectance
  publication-title: Appl. Opt.
  doi: 10.1364/AO.16.001151
  contributor:
    fullname: Tucker
– volume: 38
  start-page: 259
  year: 1995
  ident: 10.1016/j.eja.2018.12.004_bib0225
  article-title: Color indices for weed identification under various soil, residue, and lighting conditions
  publication-title: Trans. Am. Soc. Agric. Eng.
  doi: 10.13031/2013.27838
  contributor:
    fullname: Woebbecke
– start-page: 1
  year: 2017
  ident: 10.1016/j.eja.2018.12.004_bib0060
  article-title: A simple visible and near-infrared (V-NIR) camera system for monitoring the leaf area index and growth stage of Italian ryegrass
  publication-title: Comput. Electron. Agric.
  contributor:
    fullname: Fan
– volume: 154
  start-page: 18
  year: 2018
  ident: 10.1016/j.eja.2018.12.004_bib0135
  article-title: A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network
  publication-title: Comput. Electron. Agric.
  doi: 10.1016/j.compag.2018.08.048
  contributor:
    fullname: Ma
– volume: 13
  start-page: 1
  year: 2017
  ident: 10.1016/j.eja.2018.12.004_bib0230
  article-title: Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization
  publication-title: Plant Methods
  doi: 10.1186/s13007-017-0254-7
  contributor:
    fullname: Xiong
– volume: 159
  start-page: 21
  year: 2014
  ident: 10.1016/j.eja.2018.12.004_bib0055
  article-title: LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status
  publication-title: Field Crop. Res.
  doi: 10.1016/j.fcr.2014.01.008
  contributor:
    fullname: Eitel
– volume: 157
  start-page: 35
  year: 2017
  ident: 10.1016/j.eja.2018.12.004_bib0030
  article-title: A real-time ultrasonic system to measure wild blueberry plant height during harvesting
  publication-title: Biosyst. Eng.
  doi: 10.1016/j.biosystemseng.2017.02.004
  contributor:
    fullname: Chang
– volume: 74
  start-page: 229
  year: 2000
  ident: 10.1016/j.eja.2018.12.004_bib0045
  article-title: Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance
  publication-title: Remote Sens. Environ.
  doi: 10.1016/S0034-4257(00)00113-9
  contributor:
    fullname: Daughtry
– volume: 521
  start-page: 436
  issue: 7553
  year: 2015
  ident: 10.1016/j.eja.2018.12.004_bib0120
  article-title: Deep learning
  publication-title: Nature
  doi: 10.1038/nature14539
  contributor:
    fullname: LeCun
– volume: 15
  start-page: 2920
  year: 2015
  ident: 10.1016/j.eja.2018.12.004_bib0160
  article-title: Estimation of biomass and canopy height in Bermudagrass, Alfalfa, and wheat using ultrasonic, laser, and spectral sensors
  publication-title: Sensors (Switzerland)
  doi: 10.3390/s150202920
  contributor:
    fullname: Pittman
– volume: 8
  start-page: 127
  year: 1979
  ident: 10.1016/j.eja.2018.12.004_bib0210
  article-title: Red and photographic infrared linear combinations for monitoring vegetation
  publication-title: Remote Sens. Environ.
  doi: 10.1016/0034-4257(79)90013-0
  contributor:
    fullname: Tucker
– start-page: 1
  year: 2018
  ident: 10.1016/j.eja.2018.12.004_bib0075
  article-title: An explainable deep machine vision framework for plant stress phenotyping
  publication-title: Proc. Natl. Acad. Sci. U. S. A.
  contributor:
    fullname: Ghosal
– volume: 129
  start-page: 341
  year: 2015
  ident: 10.1016/j.eja.2018.12.004_bib0100
  article-title: Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter
  publication-title: Biosyst. Eng.
  doi: 10.1016/j.biosystemseng.2014.11.007
  contributor:
    fullname: Jannoura
– volume: 4
  start-page: 212
  year: 2016
  ident: 10.1016/j.eja.2018.12.004_bib0220
  article-title: Estimation of biomass in wheat using random forest regression algorithm and remote sensing data
  publication-title: Crop J.
  doi: 10.1016/j.cj.2016.01.008
  contributor:
    fullname: Wang
– year: 2014
  ident: 10.1016/j.eja.2018.12.004_bib0200
  contributor:
    fullname: Simonyan
– volume: 56
  start-page: 7
  year: 2014
  ident: 10.1016/j.eja.2018.12.004_bib0025
  article-title: Conventional digital cameras as a tool for assessing leaf area index and biomass for cereal breeding
  publication-title: J. Integr. Plant Biol.
  doi: 10.1111/jipb.12117
  contributor:
    fullname: Casadesús
– volume: 127
  start-page: 418
  year: 2016
  ident: 10.1016/j.eja.2018.12.004_bib0095
  article-title: Deep learning for plant identification using vein morphological patterns
  publication-title: Comput. Electron. Agric.
  doi: 10.1016/j.compag.2016.07.003
  contributor:
    fullname: Grinblat
– volume: 73
  start-page: 1141
  issue: 10
  year: 2007
  ident: 10.1016/j.eja.2018.12.004_bib0040
  article-title: Estimating grassland biomass using svm band shaving of hyperspectral data
  publication-title: Photogramm. Eng. Rem. Sens.
  doi: 10.14358/PERS.73.10.1141
  contributor:
    fullname: Clevers
– volume: 74
  start-page: 75
  year: 2016
  ident: 10.1016/j.eja.2018.12.004_bib0180
  article-title: Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?
  publication-title: Eur. J. Agron.
  doi: 10.1016/j.eja.2015.11.026
  contributor:
    fullname: Rasmussen
– year: 2014
  ident: 10.1016/j.eja.2018.12.004_bib0085
  article-title: Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain
  publication-title: Int. J. Appl. Earth Obs. Geoinf.
  doi: 10.1016/j.jag.2014.05.006
  contributor:
    fullname: Gnyp
– volume: 125
  start-page: 50
  year: 2017
  ident: 10.1016/j.eja.2018.12.004_bib0150
  article-title: Estimating rice yield related traits and quantitative trait loci analysis under different nitrogen treatments using a simple tower-based field phenotyping system with modified single-lens reflex cameras
  publication-title: ISPRS J. Photogramm. Remote Sens.
  doi: 10.1016/j.isprsjprs.2017.01.010
  contributor:
    fullname: Naito
– volume: 45
  start-page: 5
  issue: 1
  year: 2001
  ident: 10.1016/j.eja.2018.12.004_bib0020
  article-title: Random forests
  publication-title: Mach. Learn.
  doi: 10.1023/A:1010933404324
  contributor:
    fullname: Breiman
SSID ssj0008254
Score 2.531473
Snippet •A deep convolutional neural network is proposed to the estimate above ground biomass for winter wheat at early growth stages.•The proposed method is using RGB...
SourceID crossref
elsevier
SourceType Aggregation Database
Publisher
StartPage 117
SubjectTerms Above ground biomass
Deep convolutional neural network
RGB images
Winter wheat
Title Estimating above ground biomass of winter wheat at early growth stages using digital images and deep convolutional neural network
URI https://dx.doi.org/10.1016/j.eja.2018.12.004
Volume 103
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NS8MwFA9jXvQgfuL8GDl4Euqa5WPNcYhjKu6k4K00yWudhzrmdDfB_9z3uhYV8SKUlqZNKC_Jy--lv_ceY6d5X0KmtIu0BzRQtFdR5rSOsswIrwcuSQQ5ON9OzPheXT_ohxa7aHxhiFZZ6_6VTq-0dV3Sq6XZm02nPZyHRlSAPsETwhjyYNcxZTE4f_-ieZAFVCVYMUQdikXzZ7PieMEThR4SSbUjWOdq-7U2fVtvRltsswaKfLj6lm3WgnKHbQyLeR0sA3bZxyXOT0KcZcGxL9-Ak49GGTj51CMo5s85X1I8iDlfks7leAAFNKb3lotHjsiwgBdO3PeCh2lBCUQ4tkiFGbYTAGaceOn1-MSnFP-yulTs8T12P7q8uxhHdUqFyEsjFxGaQ5n31ihEAggFlU6cFdaboF0u4yzRWSxtrj3KVIcgpYLYgfNBejMwcR_kPmuXzyUcMJ4YH3IJwssYlM0HTuZkaAvrLKIqZTvsrBFmOltFzkgbStlTipJPSfKp6Kco-Q5TjbjTH92fomb_u9rh_6odsXW8syvy9TFrL-avcILYYuG61eDpsrXh1c148gnf8M_k
link.rule.ids 314,780,784,4502,24116,27924,27925,45585,45679
linkProvider Elsevier
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8QwEA6rHtSD-MS3OXgS6jabxzZHEWXV1ZOCt9Ck07p7qMu6ujfBf-5Mt0VFvAilhaQJZZJMvkm_mWHsOO9ISJX2kQ6ABooOKkq91lGaGhF01yeJIAfn2zvTe1DXj_qxxc4bXxiiVda6f6bTK21dl7RrabZHg0Eb16ERFaBP8IYwZo4tKI3oFyf16fsXz4NMoCrDiiHuUCyaX5sVyQuGFHtIJNWRYJ2s7dfm9G3DuVxlKzVS5Gezj1ljLSjX2fJZMa6jZcAG-7jABUqQsyw4DuYbcHLSKDNOTvWIivlzzqcUEGLMp6R0OV5AEY3pvenkiSM0LOCFE_m94NmgoAwiHHukwhT7yQBGnIjp9QTFWgqAWT0q-vgme7i8uD_vRXVOhShIIycR2kNpCNYohAKIBZVOvBU2mEz7XMZpotNY2lwHFKrOMikVxB58yGQwXRN3QG6x-fK5hG3GExOyXIIIMgZl866XOVnawnqLsErZHXbSCNONZqEzXMMpGzqUvCPJO9FxKPkdphpxux_j71C1_91s93_Njthi7_627_pXdzd7bAlr7IyJvc_mJ-NXOECgMfGH1UT6BO_J0YE
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=Estimating+above+ground+biomass+of+winter+wheat+at+early+growth+stages+using+digital+images+and+deep+convolutional+neural+network&rft.jtitle=European+journal+of+agronomy&rft.au=Ma%2C+Juncheng&rft.au=Li%2C+Yunxia&rft.au=Chen%2C+Yunqiang&rft.au=Du%2C+Keming&rft.date=2019-02-01&rft.issn=1161-0301&rft.volume=103&rft.spage=117&rft.epage=129&rft_id=info:doi/10.1016%2Fj.eja.2018.12.004&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_eja_2018_12_004
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1161-0301&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1161-0301&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1161-0301&client=summon