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Volumn 14, Issue 1, 2018, Pages

Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging

Author keywords

Deep learning; Phenotyping; Precision agriculture; Wheat

Indexed keywords


EID: 85043775154     PISSN: None     EISSN: 17464811     Source Type: Journal    
DOI: 10.1186/s13007-018-0287-6     Document Type: Article
Times cited : (93)

References (38)
  • 1
    • 84887105216 scopus 로고    scopus 로고
    • Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps
    • Mulla DJ. Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosyst Eng. 2013;114(4):358-71.
    • (2013) Biosyst Eng , vol.114 , Issue.4 , pp. 358-371
    • Mulla, D.J.1
  • 2
    • 85015673933 scopus 로고    scopus 로고
    • Mapping drought-impacted vegetation stress in california using remote sensing
    • Rao M, Silber-Coats Z, Powers S, Fox L III, Ghulam A. Mapping drought-impacted vegetation stress in california using remote sensing. GIsci Remote Sens. 2017;54(2):185-201.
    • (2017) GIsci Remote Sens , vol.54 , Issue.2 , pp. 185-201
    • Rao, M.1    Silber-Coats, Z.2    Powers, S.3    Fox, L.4    Ghulam, A.5
  • 3
    • 84868629775 scopus 로고    scopus 로고
    • The application of small unmanned aerial systems for precision agriculture: a review
    • Zhang C, Kovacs JM. The application of small unmanned aerial systems for precision agriculture: a review. Precis Agric. 2012;13(6):693-712.
    • (2012) Precis Agric , vol.13 , Issue.6 , pp. 693-712
    • Zhang, C.1    Kovacs, J.M.2
  • 9
    • 84939166594 scopus 로고    scopus 로고
    • Sensitivity analysis of vegetation indices to drought over two tallgrass prairie sites
    • Bajgain R, Xiao X, Wagle P, Basara J, Zhou Y. Sensitivity analysis of vegetation indices to drought over two tallgrass prairie sites. ISPRS J Photogramm Remote Sens. 2015;108:151-60.
    • (2015) ISPRS J Photogramm Remote Sens , vol.108 , pp. 151-160
    • Bajgain, R.1    Xiao, X.2    Wagle, P.3    Basara, J.4    Zhou, Y.5
  • 10
    • 85043765967 scopus 로고    scopus 로고
    • Use of vegetation indices to detect plant diseases
    • In: GIL Jahrestagung
    • Gröll K, Graeff S, Claupein W. Use of vegetation indices to detect plant diseases. In: GIL Jahrestagung. 2007. p. 95-8. https://subs.emis.de/LNI/Proceedings/Proceedings101/article1354.html.
    • (2007) , pp. 95-98
    • Gröll, K.1    Graeff, S.2    Claupein, W.3
  • 12
    • 85035787290 scopus 로고    scopus 로고
    • Comparative performance of ground versus aerially assessed rgb and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization
    • Gracia-Romero A, Kefauver SC, Vergara-Diaz O, Zaman-Allah MA, Prasanna BM, Cairns JE, Araus JL. Comparative performance of ground versus aerially assessed rgb and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization. Front Plant Sci. 2017;8:2004.
    • (2017) Front Plant Sci , vol.8 , pp. 2004
    • Gracia-Romero, A.1    Kefauver, S.C.2    Vergara-Diaz, O.3    Zaman-Allah, M.A.4    Prasanna, B.M.5    Cairns, J.E.6    Araus, J.L.7
  • 13
    • 79958119857 scopus 로고    scopus 로고
    • Use of normalised difference vegetation index, nitrogen concentration, and total nitrogen content of whole maize plant and plant fractions to estimate yield and nutritive value of hybrid forage maize
    • Islam MR, Garcia SCY, Henry D. Use of normalised difference vegetation index, nitrogen concentration, and total nitrogen content of whole maize plant and plant fractions to estimate yield and nutritive value of hybrid forage maize. Crop Pasture Sci. 2011;62(5):374-82.
    • (2011) Crop Pasture Sci , vol.62 , Issue.5 , pp. 374-382
    • Islam, M.R.1    Garcia, S.C.Y.2    Henry, D.3
  • 14
    • 80051774000 scopus 로고    scopus 로고
    • Application of vegetation indices for agricultural crop yield prediction using neural network techniques
    • Panda SS, Ames DP, Panigrahi S. Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sens. 2010;2(3):673-96.
    • (2010) Remote Sens , vol.2 , Issue.3 , pp. 673-696
    • Panda, S.S.1    Ames, D.P.2    Panigrahi, S.3
  • 16
    • 0030453414 scopus 로고    scopus 로고
    • Use of a green channel in remote sensing of global vegetation from EOS-MODIS
    • Gitelson AA, Kaufman YJ, Merzlyak MN. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ. 1996;58(3):289-98.
    • (1996) Remote Sens Environ , vol.58 , Issue.3 , pp. 289-298
    • Gitelson, A.A.1    Kaufman, Y.J.2    Merzlyak, M.N.3
  • 17
    • 84872176008 scopus 로고    scopus 로고
    • A red-edge spectral index for remote sensing estimation of green lai over agroecosystems
    • Delegido J, Verrelst J, Meza CM, Rivera JP, Alonso L, Moreno J. A red-edge spectral index for remote sensing estimation of green lai over agroecosystems. Eur J Agron. 2013;46:42-52.
    • (2013) Eur J Agron , vol.46 , pp. 42-52
    • Delegido, J.1    Verrelst, J.2    Meza, C.M.3    Rivera, J.P.4    Alonso, L.5    Moreno, J.6
  • 18
    • 0024165401 scopus 로고
    • A soil-adjusted vegetation index (SAVI)
    • Huete AR. A soil-adjusted vegetation index (SAVI). Remote Sens Environ. 1988;25(3):295-309.
    • (1988) Remote Sens Environ , vol.25 , Issue.3 , pp. 295-309
    • Huete, A.R.1
  • 19
    • 0036846393 scopus 로고    scopus 로고
    • Overview of the radiometric and biophysical performance of the modis vegetation indices
    • Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. Overview of the radiometric and biophysical performance of the modis vegetation indices. Remote Sens Environ. 2002;83(1-2):195-213.
    • (2002) Remote Sens Environ , vol.83 , Issue.1-2 , pp. 195-213
    • Huete, A.1    Didan, K.2    Miura, T.3    Rodriguez, E.P.4    Gao, X.5    Ferreira, L.G.6
  • 21
    • 81055126659 scopus 로고    scopus 로고
    • Getting NDVI spectral bands from a single standard RGB digital camera: a methodological approach
    • In: Conference of the Spanish association for artificial intelligence. Berlin: Springer
    • Rabatel G, Gorretta N, Labbé S. Getting NDVI spectral bands from a single standard RGB digital camera: a methodological approach. In: Conference of the Spanish association for artificial intelligence. Berlin: Springer; 2011. p. 333-342
    • (2011) , pp. 333-342
    • Rabatel, G.1    Gorretta, N.2    Labbé, S.3
  • 22
    • 84875176667 scopus 로고    scopus 로고
    • Strategy for the development of a smart NDVI camera system for outdoor plant detection and agricultural embedded systems
    • Dworak V, Selbeck J, Dammer K-H, Hoffmann M, Zarezadeh AA, Bobda C. Strategy for the development of a smart NDVI camera system for outdoor plant detection and agricultural embedded systems. Sensors. 2013;13(2):1523-38.
    • (2013) Sensors , vol.13 , Issue.2 , pp. 1523-1538
    • Dworak, V.1    Selbeck, J.2    Dammer, K.-H.3    Hoffmann, M.4    Zarezadeh, A.A.5    Bobda, C.6
  • 23
    • 84873971128 scopus 로고    scopus 로고
    • Small format optical sensors for measuring vegetation indices in remote sensing applications: a comparative approach
    • Muda MA, Foulonneau A, Bigue L, Sudibyo H, Sudiana D. Small format optical sensors for measuring vegetation indices in remote sensing applications: a comparative approach. In: TENCON region 10 conference. IEEE; 2012. p. 1-6.
    • (2012) In: TENCON region 10 conference. IEEE , pp. 1-6
    • Muda, M.A.1    Foulonneau, A.2    Bigue, L.3    Sudibyo, H.4    Sudiana, D.5
  • 24
    • 84910651844 scopus 로고    scopus 로고
    • Deep learning in neural networks: an overview
    • Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85-117.
    • (2015) Neural Netw , vol.61 , pp. 85-117
    • Schmidhuber, J.1
  • 25
    • 85040715789 scopus 로고    scopus 로고
    • The use of plant models in deep learning: An application to leaf counting in rosette plants
    • Ubbens J, Cieslak M, Prusinkiewicz P, Stavness I. The use of plant models in deep learning: an application to leaf counting in rosette plants. Plant Methods. 2018;14(1):6.
    • (2018) Plant Methods , vol.14 , Issue.1 , pp. 6
    • Ubbens, J.1    Cieslak, M.2    Prusinkiewicz, P.3    Stavness, I.4
  • 26
    • 85033404576 scopus 로고    scopus 로고
    • TasselNet: counting maize tassels in the wild via local counts regression network
    • Lu H, Cao Z, Xiao Y, Zhuang B, Shen C. TasselNet: counting maize tassels in the wild via local counts regression network. Plant Methods. 2017;13(1):79.
    • (2017) Plant Methods , vol.13 , Issue.1 , pp. 79
    • Lu, H.1    Cao, Z.2    Xiao, Y.3    Zhuang, B.4    Shen, C.5
  • 27
    • 85053578756 scopus 로고    scopus 로고
    • Cotton bloom detection using aerial images and convolutional neural network
    • Xu R, Li C, Paterson A, Jiang Y, Sun S, Robertson J. Cotton bloom detection using aerial images and convolutional neural network. Front Plant Sci. 2017;8:2235.
    • (2017) Front Plant Sci , vol.8 , pp. 2235
    • Xu, R.1    Li, C.2    Paterson, A.3    Jiang, Y.4    Sun, S.5    Robertson, J.6
  • 28
    • 85026457441 scopus 로고    scopus 로고
    • Deep plant phenomics: a deep learning platform for complex plant phenotyping tasks
    • Ubbens JR, Stavness I. Deep plant phenomics: a deep learning platform for complex plant phenotyping tasks. Front Plant Sci. 2017;8:1190.
    • (2017) Front Plant Sci , vol.8 , pp. 1190
    • Ubbens, J.R.1    Stavness, I.2
  • 30
    • 85035097883 scopus 로고    scopus 로고
    • Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization
    • Xiong X, Duan L, Liu L, Tu H, Yang P, Wu D, Chen G, Xiong L, Yang W, Liu Q. Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization. Plant Methods. 2017;13(1):104.
    • (2017) Plant Methods , vol.13 , Issue.1 , pp. 104
    • Xiong, X.1    Duan, L.2    Liu, L.3    Tu, H.4    Yang, P.5    Wu, D.6    Chen, G.7    Xiong, L.8    Yang, W.9    Liu, Q.10
  • 31
    • 85038942809 scopus 로고    scopus 로고
    • Fine-grained recognition of plants from images
    • Šulc M, Matas J. Fine-grained recognition of plants from images. Plant Methods. 2017;13(1):115.
    • (2017) Plant Methods , vol.13 , Issue.1 , pp. 115
    • Šulc, M.1    Matas, J.2
  • 32
    • 84988564472 scopus 로고    scopus 로고
    • Using deep learning for image-based plant disease detection
    • Mohanty SP, Hughes DP, Salathé M. Using deep learning for image-based plant disease detection. Front Plant Sci. 2016;7:1419.
    • (2016) Front Plant Sci , vol.7 , pp. 1419
    • Mohanty, S.P.1    Hughes, D.P.2    Salathé, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.