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Volumn 6, Issue 11, 2017, Pages

Plant phenomics: An overview of image acquisition technologies and image data analysis algorithms

Author keywords

Algorithms; Artificial vision; Deep learning; Hyperspectral cameras; Machine learning; Segmentation

Indexed keywords

ALGORITHM; AUTOMATION; BIOMICS; DATA ANALYSIS; DATA EXTRACTION; FLUORESCENCE IMAGING; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE SEGMENTATION; INFORMATION PROCESSING; MACHINE LEARNING; NONHUMAN; PHENOMICS; PHENOTYPE; PLANT; PRIORITY JOURNAL; REVIEW; SOFTWARE; THERMOGRAPHY; TOMOGRAPHY; GENETICS; GENOMICS; PLANT GENOME; PROCEDURES;

EID: 85042182943     PISSN: None     EISSN: 2047217X     Source Type: Journal    
DOI: 10.1093/gigascience/gix092     Document Type: Review
Times cited : (138)

References (233)
  • 1
    • 0018465733 scopus 로고
    • Red and photographic infrared linear combinations for monitoring vegetation
    • Accessed 11 October 2016.
    • Tucker C. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 1979. Available at: http://www.sciencedirect.com/science/article/pii/0034425779900130. Accessed 11 October 2016.
    • (1979) Remote Sens Environ
    • Tucker, C.1
  • 2
    • 0030619096 scopus 로고    scopus 로고
    • Increased plant growth in the northern high latitudes from 1981 to 1991
    • Myneni RB, Keeling CD, Tucker CJ et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 1997;386:698-702.
    • (1997) Nature , vol.386 , pp. 698-702
    • Myneni, R.B.1    Keeling, C.D.2    Tucker, C.J.3
  • 3
    • 0028582679 scopus 로고
    • NDVI-derived land cover classifications at a global scale
    • Accessed 11 October 2016.
    • DeFries R, Townshend J. NDVI-derived land cover classifications at a global scale. Int J Remote 1994. Available at: http://www.tandfonline.com/doi/abs/10.1080/0143116940 8954345. Accessed 11 October 2016.
    • (1994) Int J Remote
    • DeFries, R.1    Townshend, J.2
  • 4
    • 23944523791 scopus 로고    scopus 로고
    • Using the satellitederived NDVI to assess ecological responses to environmental change
    • Accessed 11 October 2016.
    • Pettorelli N, Vik J, Mysterud A et al. Using the satellitederived NDVI to assess ecological responses to environmental change. Trends Ecol 2005. Available at: http://www. sciencedirect.com/science/article/pii/S016953470500162X. Accessed 11 October 2016.
    • (2005) Trends Ecol
    • Pettorelli, N.1    Vik, J.2    Mysterud, A.3
  • 5
    • 78651435852 scopus 로고    scopus 로고
    • Crop yield forecasting on the Canadian Prairies using MODIS NDVI data
    • Mkhabela MS, Bullock P, Raj S et al. Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agric For Meteorol 2011;151:385-93.
    • (2011) Agric For Meteorol , vol.151 , pp. 385-393
    • Mkhabela, M.S.1    Bullock, P.2    Raj, S.3
  • 6
    • 0027788571 scopus 로고
    • NDVI-crop monitoring and early yield assessment of Burkina Faso
    • GROTEN SME. NDVI-crop monitoring and early yield assessment of Burkina Faso. Int J Remote Sens 1993;14:1495-515.
    • (1993) Int J Remote Sens , vol.14 , pp. 1495-1515
  • 7
    • 0036840227 scopus 로고    scopus 로고
    • Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine
    • Jones HG, Stoll M, Santos T et al. Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine. J Exp Bot 2002;53:2249-60.
    • (2002) J Exp Bot , vol.53 , pp. 2249-2260
    • Jones, H.G.1    Stoll, M.2    Santos, T.3
  • 9
    • 70449673080 scopus 로고    scopus 로고
    • A new screening method for osmotic component of salinity tolerance in cereals using infrared thermography
    • Sirault XRR, James RA, Furbank RT et al. A new screening method for osmotic component of salinity tolerance in cereals using infrared thermography. Funct Plant Biol 2009;36:970.
    • (2009) Funct Plant Biol , vol.36 , pp. 970
    • Sirault, X.R.R.1    James, R.A.2    Furbank, R.T.3
  • 10
    • 80052424959 scopus 로고    scopus 로고
    • A novel image analysis toolbox enabling quantitative analysis of root system architecture
    • Lobet G, Pagès L, Draye X. A novel image analysis toolbox enabling quantitative analysis of root system architecture. Plant Physiol 2011;157:29-39.
    • (2011) Plant Physiol , vol.157 , pp. 29-39
    • Lobet, G.1    Pagès, L.2    Draye, X.3
  • 11
    • 84864146481 scopus 로고    scopus 로고
    • GiA Roots: software for the high throughput analysis of plant root system architecture
    • Galkovskyi T, Mileyko Y, Bucksch A et al. GiA Roots: software for the high throughput analysis of plant root system architecture. BMC Plant Biol 2012;12:116.
    • (2012) BMC Plant Biol , vol.12 , pp. 116
    • Galkovskyi, T.1    Mileyko, Y.2    Bucksch, A.3
  • 12
    • 68249155323 scopus 로고    scopus 로고
    • Highthroughput quantification of root growth using a novel image-analysis tool
    • French A, Ubeda-Tomas S, Holman TJ et al. Highthroughput quantification of root growth using a novel image-analysis tool. Plant Physiol 2009;150:1784-95.
    • (2009) Plant Physiol , vol.150 , pp. 1784-1795
    • French, A.1    Ubeda-Tomas, S.2    Holman, T.J.3
  • 13
    • 79351470088 scopus 로고    scopus 로고
    • Accurate inference of shoot biomass from high-throughput images of cereal plants
    • Golzarian MR, Frick RA, Rajendran K et al. Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 2011;7:2.
    • (2011) Plant Methods , vol.7 , pp. 2
    • Golzarian, M.R.1    Frick, R.A.2    Rajendran, K.3
  • 14
    • 79955684378 scopus 로고    scopus 로고
    • PHENOPSIS DB: an information system for Arabidopsis thaliana phenotypic data in an environmental context
    • Fabre J, Dauzat M, Negre V et al. PHENOPSIS DB: an information system for Arabidopsis thaliana phenotypic data in an environmental context. BMC Plant Biol 2011;11.
    • (2011) BMC Plant Biol , vol.11
    • Fabre, J.1    Dauzat, M.2    Negre, V.3
  • 15
    • 84891372768 scopus 로고    scopus 로고
    • Field high-throughput phenotyping: the new crop breeding frontier
    • Araus JL, Cairns JE. Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 2014;19:52-61.
    • (2014) Trends Plant Sci , vol.19 , pp. 52-61
    • Araus, J.L.1    Cairns, J.E.2
  • 16
    • 70449678706 scopus 로고    scopus 로고
    • Plant phenomics: from gene to form and function
    • Furbank RT. Plant phenomics: from gene to form and function. Funct Plant Biol 2009;36:V-Vi.
    • (2009) Funct Plant Biol , vol.36 , pp. V-Vi
    • Furbank, R.T.1
  • 17
    • 84994406863 scopus 로고    scopus 로고
    • Tansley review: Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field
    • Poorter H, Fiorani F, Pieruschka R et al. Tansley review: Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field. New Phytol 2016;838-55.
    • (2016) New Phytol , pp. 838-855
    • Poorter, H.1    Fiorani, F.2    Pieruschka, R.3
  • 18
    • 84878552883 scopus 로고    scopus 로고
    • Plant phenomics and highthroughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies
    • Yang W, Duan L, Chen G et al. Plant phenomics and highthroughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies. Curr Opin Plant Biol 2013;16:180-7.
    • (2013) Curr Opin Plant Biol , vol.16 , pp. 180-187
    • Yang, W.1    Duan, L.2    Chen, G.3
  • 19
    • 84860329380 scopus 로고    scopus 로고
    • Field-based phenomics for plant genetics research
    • Accessed 31 August 2016.
    • White J, Andrade-Sanchez P, Gore M. Field-based phenomics for plant genetics research. F Crop 2012. Available at: http://www.sciencedirect.com/science/article/pii/S037842901200130X. Accessed 31 August 2016.
    • (2012) F Crop
    • White, J.1    Andrade-Sanchez, P.2    Gore, M.3
  • 20
    • 84923536786 scopus 로고    scopus 로고
    • Lights, camera, action: high-throughput plant phenotyping is ready for a close-up
    • Fahlgren N, Gehan MA, Baxter I. Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol 2015:93-9.
    • (2015) Curr Opin Plant Biol , pp. 93-99
    • Fahlgren, N.1    Gehan, M.A.2    Baxter, I.3
  • 21
    • 83055180602 scopus 로고    scopus 로고
    • Phenomics-technologies to relieve the phenotyping bottleneck
    • Furbank RT, Tester M. Phenomics-technologies to relieve the phenotyping bottleneck. Trends Plant Sci 2011;16: 635-44.
    • (2011) Trends Plant Sci , vol.16 , pp. 635-644
    • Furbank, R.T.1    Tester, M.2
  • 22
    • 84896123655 scopus 로고    scopus 로고
    • Phenotyping and beyond: modelling the relationships between traits
    • Granier C, Vile D. Phenotyping and beyond: modelling the relationships between traits. Curr Opin Plant Biol 2014;18:96-102.
    • (2014) Curr Opin Plant Biol , vol.18 , pp. 96-102
    • Granier, C.1    Vile, D.2
  • 23
    • 84860329380 scopus 로고    scopus 로고
    • Field-based phenomics for plant genetics research
    • White J, Andrade-Sanchez P, Gore M et al. Field-based phenomics for plant genetics research. F Crop 2012;133:101-12.
    • (2012) F Crop , vol.133 , pp. 101-112
    • White, J.1    Andrade-Sanchez, P.2    Gore, M.3
  • 24
    • 83055180602 scopus 로고    scopus 로고
    • Phenomics-technologies to relieve the phenotyping bottleneck
    • Furbank RT, Tester M. Phenomics-technologies to relieve the phenotyping bottleneck. Trends Plant Sci 2011;16: 635-44.
    • (2011) Trends Plant Sci , vol.16 , pp. 635-644
    • Furbank, R.T.1    Tester, M.2
  • 25
    • 85006995905 scopus 로고    scopus 로고
    • Phenomic approaches and tools for phytopathologists
    • Accessed 28 August 2017
    • Simko I, Jimenez-Berni JA, Sirault XRR. Phenomic approaches and tools for phytopathologists. Phytopathology 2016. Available at: http://apsjournals.apsnet.org/doi/10. 1094/PHYTO-02-16-0082-RVW. Accessed 28 August 2017.
    • (2016) Phytopathology
    • Simko, I.1    Jimenez-Berni, J.A.2    Sirault, X.R.R.3
  • 26
    • 85042199748 scopus 로고    scopus 로고
    • Monitoring photosynthesis by in vivo chlorophyll fluorescence: application to high-throughput plant phenotyping
    • Intech.
    • da Silva Marques J. Monitoring photosynthesis by in vivo chlorophyll fluorescence: application to high-throughput plant phenotyping. Appl Photosynth-New Prog 2016; Intech:3-22.
    • (2016) Appl Photosynth-New Prog , pp. 3-22
    • da Silva Marques, J.1
  • 27
    • 0003626435 scopus 로고    scopus 로고
    • Digital Image Processing
    • Upper Saddle River, New Jersey, USA: Prentice Hall Press
    • Gonzalez RC, Woods RE. Digital Image Processing. Upper Saddle River, New Jersey, USA: Prentice Hall Press; 2002.
    • (2002)
    • Gonzalez, R.C.1    Woods, R.E.2
  • 28
    • 0003403091 scopus 로고
    • The image processing handbook
    • Accessed 25 April 2017.
    • Russ J, Woods R. The image processing handbook. 1995. Available at: http://journals.lww.com/jcat/Citation/1995/11000/The Image Processing Handbook 2nd Ed.26.aspx. Accessed 25 April 2017.
    • (1995)
    • Russ, J.1    Woods, R.2
  • 29
    • 0003409571 scopus 로고
    • Fundamentals of digital image processing
    • Accessed 25 April 2017.
    • Jain A. Fundamentals of digital image processing. 1989. Available at: http://dl.acm.org/citation.cfm?id=59921. Accessed 25 April 2017.
    • (1989)
    • Jain, A.1
  • 30
    • 0003557270 scopus 로고    scopus 로고
    • Image Processing, Analysis, and Machine Vision
    • 4th ed. CL Engineering. Berlin, Germany: Springer, Accessed 24 April 2017.
    • Sonka M, Hlavac V, Boyle R. Image Processing, Analysis, and Machine Vision. 4th ed. CL Engineering. Berlin, Germany: Springer; 2014. Available at: https://books.google.es/books?hl=en&lr=&id=QePKAgAAQBAJ&oi=fnd& pg=PR11&dq=image+analysis+a+review&ots=95qB21F9B-&sig=kSGTMS9GfxkddVJUHnxnBzU2VL8. Accessed 24 April 2017.
    • (2014)
    • Sonka, M.1    Hlavac, V.2    Boyle, R.3
  • 31
    • 0003826720 scopus 로고    scopus 로고
    • Morphological Image Analysis: Principles and Applications
    • Berlin, Germany: Springer, Accessed 25 April 2017.
    • Soille P. Morphological Image Analysis: Principles and Applications. Berlin, Germany: Springer; 2013. Available at: https://books.google.es/books?hl=en&lr=&id=ZFzxCAAAQ BAJ&oi=fnd&pg=PA1&dq=image+analysis+a+review&ots=-oc-0SEZ6g&sig=wLoRbdNSusr-5UtgDRvtMHVqjQ. Accessed 25 April 2017.
    • (2013)
    • Soille, P.1
  • 32
    • 84908530182 scopus 로고    scopus 로고
    • A review of imaging techniques for plant phenotyping
    • Li L, Zhang Q, Huang D. A review of imaging techniques for plant phenotyping. Sensors 2014;14:20078-111.
    • (2014) Sensors , vol.14 , pp. 20078-20111
    • Li, L.1    Zhang, Q.2    Huang, D.3
  • 33
    • 84958049448 scopus 로고    scopus 로고
    • Machine learning for high-throughput stress phenotyping in plants
    • Singh A, Ganapathysubramanian B, Singh AK et al. Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 2016;110-24.
    • (2016) Trends Plant Sci , pp. 110-124
    • Singh, A.1    Ganapathysubramanian, B.2    Singh, A.K.3
  • 34
    • 84877682482 scopus 로고    scopus 로고
    • Future scenarios for plant phenotyping
    • Fiorani F, Schurr U. Future scenarios for plant phenotyping. Annu Rev Plant Biol 2013;64:267-91.
    • (2013) Annu Rev Plant Biol , vol.64 , pp. 267-291
    • Fiorani, F.1    Schurr, U.2
  • 35
  • 36
    • 84988723417 scopus 로고    scopus 로고
    • A low power digital accumulation technique for digital-domain CMOS TDI image sensor
    • Yu C, Nie K, Xu J et al. A low power digital accumulation technique for digital-domain CMOS TDI image sensor. Sensors 2016;16:1572.
    • (2016) Sensors , vol.16 , pp. 1572
    • Yu, C.1    Nie, K.2    Xu, J.3
  • 37
    • 85042190230 scopus 로고    scopus 로고
    • Accessed 4 April
    • Teledyne Dalsa. https://www.teledynedalsa.com/corp/. Accessed 4 April 2017.
    • (2017)
  • 38
    • 85042173829 scopus 로고    scopus 로고
    • Imec launches TDI, multispectral and hyperspectral sensors
    • Accessed 24 April
    • IMEC. Imec launches TDI, multispectral and hyperspectral sensors. Available at: http://optics.org/news/8/2/8. Accessed 24 April 2017.
    • (2017)
  • 39
    • 84868699333 scopus 로고    scopus 로고
    • SPICY: towards automated phenotyping of large pepper plants in the greenhouse
    • Van Der Heijden G, Song Y, Horgan G et al. SPICY: towards automated phenotyping of large pepper plants in the greenhouse. Funct Plant Biol 2012;39:870-7.
    • (2012) Funct Plant Biol , vol.39 , pp. 870-877
    • Van Der Heijden, G.1    Song, Y.2    Horgan, G.3
  • 40
    • 84870545640 scopus 로고    scopus 로고
    • Development of a configurable growth chamber with a computer vision system to study circadian rhythm in plants
    • Navarro PJ, Fernández C, Weiss J et al. Development of a configurable growth chamber with a computer vision system to study circadian rhythm in plants. Sensors (Basel) 2012;12:15356-75.
    • (2012) Sensors (Basel) , vol.12 , pp. 15356-15375
    • Navarro, P.J.1    Fernández, C.2    Weiss, J.3
  • 41
    • 84879693910 scopus 로고    scopus 로고
    • A high throughput robot system for machine vision based plant phenotype studies
    • Subramanian R, Spalding EP, Ferrier NJ. A high throughput robot system for machine vision based plant phenotype studies. Mach Vis Appl 2013;24:619-36.
    • (2013) Mach Vis Appl , vol.24 , pp. 619-636
    • Subramanian, R.1    Spalding, E.P.2    Ferrier, N.J.3
  • 42
    • 85014607035 scopus 로고    scopus 로고
    • High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth
    • Zhang X, Huang C, Wu D et al. High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth. Plant Physiol 2017;173(3): 1554-64.
    • (2017) Plant Physiol , vol.173 , Issue.3 , pp. 1554-1564
    • Zhang, X.1    Huang, C.2    Wu, D.3
  • 43
    • 85006253072 scopus 로고    scopus 로고
    • Field Scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring
    • Virlet N, Sabermanesh K, Sadeghi-Tehran P et al. Field Scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring. Funct Plant Biol 2017;44:143.
    • (2017) Funct Plant Biol , vol.44 , pp. 143
    • Virlet, N.1    Sabermanesh, K.2    Sadeghi-Tehran, P.3
  • 44
    • 84908509157 scopus 로고    scopus 로고
    • Proximal remote sensing buggies and potential applications for field-based phenotyping
    • Deery D, Jimenez-Berni J, Jones H et al. Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 2014;4:349-79.
    • (2014) Agronomy , vol.4 , pp. 349-379
    • Deery, D.1    Jimenez-Berni, J.2    Jones, H.3
  • 45
    • 84868707545 scopus 로고    scopus 로고
    • A semi-automatic system for high throughput phenotyping wheat cultivars infield conditions: description and first results
    • Comar A, Burger P, de Solan B et al. A semi-automatic system for high throughput phenotyping wheat cultivars infield conditions: description and first results. Funct Plant Biol 2012;39:914.
    • (2012) Funct Plant Biol , vol.39 , pp. 914
    • Comar, A.1    Burger, P.2    de Solan, B.3
  • 47
    • 34548168355 scopus 로고    scopus 로고
    • A stereo imaging system for measuring structural parameters of plant canopies
    • Biskup B, Scharr H, Schurr U et al. A stereo imaging system for measuring structural parameters of plant canopies. Plant Cell Environ 2007;30:1299-308.
    • (2007) Plant Cell Environ , vol.30 , pp. 1299-1308
    • Biskup, B.1    Scharr, H.2    Schurr, U.3
  • 48
    • 84938411714 scopus 로고    scopus 로고
    • Structured light-based 3D reconstruction system for plants
    • Nguyen TT, Slaughter DC, Max N et al. Structured light-based 3D reconstruction system for plants. Sensors (Switzerland) 2015;15:18587-612.
    • (2015) Sensors (Switzerland) , vol.15 , pp. 18587-18612
    • Nguyen, T.T.1    Slaughter, D.C.2    Max, N.3
  • 49
    • 84930208782 scopus 로고    scopus 로고
    • Field phenotyping of grapevine growth using dense stereo reconstruction
    • Klodt M, Herzog K, Töpfer R et al. Field phenotyping of grapevine growth using dense stereo reconstruction. BMC Bioinformatics 2015;16:143.
    • (2015) BMC Bioinformatics , vol.16 , pp. 143
    • Klodt, M.1    Herzog, K.2    Töpfer, R.3
  • 50
    • 85006741771 scopus 로고    scopus 로고
    • Towards automated large-scale 3D phenotyping of vineyards under field conditions
    • Rose J, Kicherer A, Wieland M et al. Towards automated large-scale 3D phenotyping of vineyards under field conditions. Sensors 2016;16:2136.
    • (2016) Sensors , vol.16 , pp. 2136
    • Rose, J.1    Kicherer, A.2    Wieland, M.3
  • 51
    • 85011032040 scopus 로고    scopus 로고
    • A high-throughput stereoimaging system for quantifying rape leaf traits during the seedling stage
    • Xiong X, Yu L, Yang W et al. A high-throughput stereoimaging system for quantifying rape leaf traits during the seedling stage. Plant Methods 2017;13:7.
    • (2017) Plant Methods , vol.13 , pp. 7
    • Xiong, X.1    Yu, L.2    Yang, W.3
  • 52
    • 84860363173 scopus 로고    scopus 로고
    • A novel mesh processing based technique for 3D plant analysis
    • Paproki A, Sirault XRR, Berry S et al. A novel mesh processing based technique for 3D plant analysis. BMC Plant Biol 2012;12:63.
    • (2012) BMC Plant Biol , vol.12 , pp. 63
    • Paproki, A.1    Sirault, X.R.R.2    Berry, S.3
  • 53
    • 84982256029 scopus 로고    scopus 로고
    • Plant phenotyping using multi-view stereo vision with structured lights
    • Valasek J, Thomasson JA, eds. Baltimore, Maryland, USA: SPIE Commercial + Scientific Sensing and Imaging; Accessed 19 May 2017.
    • Nguyen TT, Slaughter DC, Maloof JN et al. Plant phenotyping using multi-view stereo vision with structured lights. In: Valasek J, Thomasson JA, eds. International Society for Optics and Photonics. Baltimore, Maryland, USA: SPIE Commercial + Scientific Sensing and Imaging; 2016:986608. Available at: http://proceedings.spiedigitallibrary.org/proceeding.aspx? doi=10.1117/12.2229513. Accessed 19 May 2017.
    • (2016) International Society for Optics and Photonics
    • Nguyen, T.T.1    Slaughter, D.C.2    Maloof, J.N.3
  • 54
    • 84928660960 scopus 로고    scopus 로고
    • Accuracy analysis of a multi-view stereo approach for phenotyping of tomato plants at the organ level
    • Rose JC, Paulus S, Kuhlmann H. Accuracy analysis of a multi-view stereo approach for phenotyping of tomato plants at the organ level. Sensors (Basel) 2015;15: 9651-65.
    • (2015) Sensors (Basel) , vol.15 , pp. 9651-9665
    • Rose, J.C.1    Paulus, S.2    Kuhlmann, H.3
  • 55
    • 85042179414 scopus 로고    scopus 로고
    • An overview of 3D plant phenotyping methods
    • Accessed 19 June 2017
    • Schwartz S. An overview of 3D plant phenotyping methods. Phenospex Smart Plant Anal 2015. Available at: https://phenospex.com/blog/an-overview-of-3d-plantphenotyping-methods/#ref. Accessed 19 June 2017.
    • (2015) Phenospex Smart Plant Anal
    • Schwartz, S.1
  • 56
    • 2942739367 scopus 로고    scopus 로고
    • Hyperspectral versus multispectral data for estimating leaf area index in four different biomes
    • Lee K-S, Cohen WB, Kennedy RE et al. Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sens Environ 2004;91:508-20.
    • (2004) Remote Sens Environ , vol.91 , pp. 508-520
    • Lee, K.-S.1    Cohen, W.B.2    Kennedy, R.E.3
  • 57
    • 3042680324 scopus 로고    scopus 로고
    • Multispectral and hyperspectral remote sensing of alpine snow properties
    • Dozier J, Painter TH. Multispectral and hyperspectral remote sensing of alpine snow properties. Annu Rev Earth Planet Sci 2004;32:465-94.
    • (2004) Annu Rev Earth Planet Sci , vol.32 , pp. 465-494
    • Dozier, J.1    Painter, T.H.2
  • 58
    • 77952881448 scopus 로고    scopus 로고
    • Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review
    • Adam E, Mutanga O, Rugege D. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetl Ecol Manag 2010;18:281-96.
    • (2010) Wetl Ecol Manag , vol.18 , pp. 281-296
    • Adam, E.1    Mutanga, O.2    Rugege, D.3
  • 59
    • 84877757536 scopus 로고    scopus 로고
    • Hyperspectral andmultispectral imaging for evaluating food safety and quality
    • Qin J, Chao K, Kim MS et al. Hyperspectral andmultispectral imaging for evaluating food safety and quality. J Food Eng 2013;118:157-71.
    • (2013) J Food Eng , vol.118 , pp. 157-171
    • Qin, J.1    Chao, K.2    Kim, M.S.3
  • 61
    • 0036508375 scopus 로고    scopus 로고
    • Detection of defects on selected apple cultivars using hyperspectral and multispectral image analysis
    • Mehl PM, Chao K, Kim M et al. Detection of defects on selected apple cultivars using hyperspectral and multispectral image analysis. Appl Eng Agric 2002;18:219.
    • (2002) Appl Eng Agric , vol.18 , pp. 219
    • Mehl, P.M.1    Chao, K.2    Kim, M.3
  • 62
    • 84943150611 scopus 로고    scopus 로고
    • Comparing hyperspectral and multispectral imagery for land classification of the lower don river, Toronto
    • MsC Thesis. Ryerson University. Accessed 27 April
    • Ferrato L-J. Comparing hyperspectral and multispectral imagery for land classification of the lower don river, Toronto. MsC Thesis. Ryerson University. Available at: http://www.geography.ryerson.ca/wayne/MSA/LisaJenFerratoMRP2012.pdf. Accessed 27 April 2017.
    • (2017)
    • Ferrato, L.-J.1
  • 63
    • 85042184277 scopus 로고    scopus 로고
    • 137-ButterflEYE NIR-Cubert-GmbH
    • Accessed 4 June
    • Cubert S. 137-ButterflEYE NIR-Cubert-GmbH. Available at: http://cubert-gmbh.com/product/s-137-butterfleye-nir/. Accessed 4 June 2017.
    • (2017)
    • Cubert, S.1
  • 64
    • 77954863802 scopus 로고    scopus 로고
    • Multispectral imaging system with interchangeable filter design
    • Kise M, Park B, HeitschmidtGWet al. Multispectral imaging system with interchangeable filter design. Comput Electron Agric 2010;72:61-8.
    • (2010) Comput Electron Agric , vol.72 , pp. 61-68
    • Kise, M.1    Park, B.2    Heitschmidt, G.W.3
  • 65
    • 85009953624 scopus 로고    scopus 로고
    • Nonlinear fusion of multispectral citrus fruit image data with information contents
    • Li P, Lee S-H, Hsu H-Y et al. Nonlinear fusion of multispectral citrus fruit image data with information contents. Sensors 2017;17:142.
    • (2017) Sensors , vol.17 , pp. 142
    • Li, P.1    Lee, S.-H.2    Hsu, H.-Y.3
  • 66
    • 84960423826 scopus 로고    scopus 로고
    • Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants
    • Wahabzada M, Mahlein A-K, Bauckhage C et al. Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants. Sci Rep 2016;6:22482.
    • (2016) Sci Rep , vol.6 , pp. 22482
    • Wahabzada, M.1    Mahlein, A.-K.2    Bauckhage, C.3
  • 67
    • 84928600188 scopus 로고    scopus 로고
    • Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions
    • Kuska M, Wahabzada M, Leucker M et al. Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions. Plant Methods 2015;11:28.
    • (2015) Plant Methods , vol.11 , pp. 28
    • Kuska, M.1    Wahabzada, M.2    Leucker, M.3
  • 68
    • 84859162422 scopus 로고    scopus 로고
    • Usability study of 3D time-of-flight cameras for automatic plant phenotyping
    • Accessed 2 May 2017.
    • Klose R, Penlington J. Usability study of 3D time-of-flight cameras for automatic plant phenotyping. Bornimer 2009. Available at: https://www.hs-osnabrueck.de/fileadmin/HSOS/Homepages/COALA/Veroeffentlichungen/2009-CBA-3DToF.pdf. Accessed 2 May 2017.
    • (2009) Bornimer
    • Klose, R.1    Penlington, J.2
  • 69
    • 79957447490 scopus 로고    scopus 로고
    • Combining Stereo and Time-of-Flight Images with Application to Automatic Plant Phenotyping
    • Berlin: Springer
    • Song Y, Glasbey CA, van der Heijden GWAM et al. Combining Stereo and Time-of-Flight Images with Application to Automatic Plant Phenotyping. Berlin: Springer; 2011:467-78.
    • (2011) , pp. 467-478
    • Song, Y.1    Glasbey, C.A.2    van der Heijden, G.W.A.M.3
  • 70
    • 80155132892 scopus 로고    scopus 로고
    • 3D modelling of leaves from color and ToF data for robotized plant measuring
    • Accessed 2 May 2017.
    • Alenyà G, Dellen B, Torras C. 3D modelling of leaves from color and ToF data for robotized plant measuring. Robot Autom (ICRA) 2011. Available at: http://ieeexplore.ieee.org/abstract/document/5980092/. Accessed 2 May 2017.
    • (2011) Robot Autom (ICRA)
    • Alenyà, G.1    Dellen, B.2    Torras, C.3
  • 71
    • 84989318613 scopus 로고    scopus 로고
    • 3D sorghum reconstructions from depth images identify QTL regulating shoot architecture
    • McCormick RF, Truong SK, Mullet JE. 3D sorghum reconstructions from depth images identify QTL regulating shoot architecture. Plant Physiol 2016;172:823-34.
    • (2016) Plant Physiol , vol.172 , pp. 823-834
    • McCormick, R.F.1    Truong, S.K.2    Mullet, J.E.3
  • 72
    • 84893932231 scopus 로고    scopus 로고
    • Low-cost 3D systems: suitable tools for plant phenotyping
    • Accessed 2 May 2017.
    • Paulus S, Behmann J, Mahlein A et al. Low-cost 3D systems: suitable tools for plant phenotyping. Sensors 2014. Available at: http://www.mdpi.com/1424-8220/14/2/3001/htm. Accessed 2 May 2017.
    • (2014) Sensors
    • Paulus, S.1    Behmann, J.2    Mahlein, A.3
  • 73
    • 84892760890 scopus 로고    scopus 로고
    • Kinect for Windows sensor components and specifications
    • Accessed 7 May 2017.
    • Microsoft. Kinect for Windows sensor components and specifications. 2010. Available at: https://msdn.microsoft. com/en-us/library/jj131033.aspx. Accessed 7 May 2017.
    • (2010)
  • 74
    • 84875178849 scopus 로고    scopus 로고
    • Rapid characterization of vegetation structure with a Microsoft Kinect sensor
    • Azzari G, GouldenM, Rusu R. Rapid characterization of vegetation structure with a Microsoft Kinect sensor. Sensors 2013;13:2384-98.
    • (2013) Sensors , vol.13 , pp. 2384-2398
    • Azzari, G.1    Goulden, M.2    Rusu, R.3
  • 75
    • 84856448240 scopus 로고    scopus 로고
    • On the use of depth camera for 3D phenotyping of entire plants
    • Chéné Y, Rousseau D, Lucidarme P et al. On the use of depth camera for 3D phenotyping of entire plants. Comput Electron Agric 2012;82:122-7.
    • (2012) Comput Electron Agric , vol.82 , pp. 122-127
    • Chéné, Y.1    Rousseau, D.2    Lucidarme, P.3
  • 76
    • 84955719890 scopus 로고    scopus 로고
    • Remote Sensing of Natural Resources
    • Accessed 9 May 2017.
    • Wang G, Weng Q. Remote Sensing of Natural Resources. Available at: https://books.google.es/books?id=wIDNBQAAQBAJ&pg=PA9&dq=Light+Detection+and+Ranging+(LIDAR)+1970s&hl=es&sa=X&ved=0ahUKEwi0mbSksePTAhVJDxoKHaKxC6UQ6AEIJjAA#v=onepage&q=LightDetectionandRanging(LIDAR)1970s&f=false. Accessed 9 May 2017.
    • Wang, G.1    Weng, Q.2
  • 77
    • 84945333626 scopus 로고    scopus 로고
    • LiDAR: an important tool for next-generation phenotyping technology of high potential for plant phenomics?
    • Lin Y. LiDAR: an important tool for next-generation phenotyping technology of high potential for plant phenomics? Comput Electron Agric 2015;119:61-73.
    • (2015) Comput Electron Agric , vol.119 , pp. 61-73
    • Lin, Y.1
  • 79
    • 0029751225 scopus 로고    scopus 로고
    • Retrieving Leaf Area Index of boreal conifer forests using Landsat TM images
    • Chen JM, Cihlar J. Retrieving Leaf Area Index of boreal conifer forests using Landsat TM images. Remote Sens Environ 1996;55:155-62.
    • (1996) Remote Sens Environ , vol.55 , pp. 155-162
    • Chen, J.M.1    Cihlar, J.2
  • 80
    • 85013671662 scopus 로고    scopus 로고
    • Predictions of tropical forest biomass and biomass growth based on stand height or canopy area are improved by Landsat-scale phenology across Puerto Rico and the U.S
    • Gwenzi D, Helmer E, Zhu X et al. Predictions of tropical forest biomass and biomass growth based on stand height or canopy area are improved by Landsat-scale phenology across Puerto Rico and the U.S. Virgin Islands. Remote Sens 2017;9:123.
    • (2017) Virgin Islands. Remote Sens , vol.9 , pp. 123
    • Gwenzi, D.1    Helmer, E.2    Zhu, X.3
  • 81
    • 80052608789 scopus 로고    scopus 로고
    • Statistical fusion of lidar, InSAR, and optical remote sensing data for forest stand height characterization: a regional-scale method based on LVIS, SRTM, Landsat ETM+, and ancillary data sets
    • Kellndorfer JM, Walker WS, LaPoint E et al. Statistical fusion of lidar, InSAR, and optical remote sensing data for forest stand height characterization: a regional-scale method based on LVIS, SRTM, Landsat ETM+, and ancillary data sets. J Geophys Res Biogeosciences 2010;115.
    • (2010) J Geophys Res Biogeosciences , vol.115
    • Kellndorfer, J.M.1    Walker, W.S.2    LaPoint, E.3
  • 82
    • 84926375539 scopus 로고    scopus 로고
    • Estimating forest biomass dynamics by integrating multi-temporal Landsat satellite images with ground and airborne LiDAR data in the Coal Valley Mine, Alberta, Canada
    • BadreldinN, Sanchez-Azofeifa A. Estimating forest biomass dynamics by integrating multi-temporal Landsat satellite images with ground and airborne LiDAR data in the Coal Valley Mine, Alberta, Canada. Remote Sens 2015;7: 2832-49.
    • (2015) Remote Sens , vol.7 , pp. 2832-2849
    • Badreldin, N.1    Sanchez-Azofeifa, A.2
  • 83
    • 84886815163 scopus 로고    scopus 로고
    • Discriminating crop, weeds and soil surfacewith a terrestrial LIDAR sensor
    • Andújar D, Rueda-Ayala V, Moreno H et al. Discriminating crop, weeds and soil surfacewith a terrestrial LIDAR sensor. Sensors (Switzerland) 2013;13:14662-75.
    • (2013) Sensors (Switzerland) , vol.13 , pp. 14662-14675
    • Andújar, D.1    Rueda-Ayala, V.2    Moreno, H.3
  • 84
    • 85017658590 scopus 로고    scopus 로고
    • In-field high-throughput phenotyping of cotton plant height using LiDAR
    • Sun S, Li C, Paterson A. In-field high-throughput phenotyping of cotton plant height using LiDAR. Remote Sens 2017;9:377.
    • (2017) Remote Sens , vol.9 , pp. 377
    • Sun, S.1    Li, C.2    Paterson, A.3
  • 85
    • 79952095848 scopus 로고    scopus 로고
    • 3-D modeling of tomato canopies using a high-resolution portable scanning lidar for extracting structural information
    • Hosoi F, Nakabayashi K, Omasa K. 3-D modeling of tomato canopies using a high-resolution portable scanning lidar for extracting structural information. Sensors (Basel) 2011;11:2166-74.
    • (2011) Sensors (Basel) , vol.11 , pp. 2166-2174
    • Hosoi, F.1    Nakabayashi, K.2    Omasa, K.3
  • 86
    • 84943139586 scopus 로고    scopus 로고
    • Computer vision based autonomous robotic system for 3D plant growth measuremen
    • Halifax, NS, Canada: IEEE;, Accessed 10 May 2017
    • Chaudhury A, Ward C, Talasaz A et al. Computer vision based autonomous robotic system for 3D plant growth measurement. In: 12th Conference on Computer and Robot Vision. Halifax, NS, Canada: IEEE; 2015; DOI: 10.1109/CRV.2015.45. Accessed 10 May 2017.
    • (2015) 12th Conference on Computer and Robot Vision
    • Chaudhury, A.1    Ward, C.2    Talasaz, A.3
  • 87
    • 84930959054 scopus 로고    scopus 로고
    • 3D laser triangulation for plant phenotyping in challenging environments
    • Kjaer KH, Ottosen C-O. 3D laser triangulation for plant phenotyping in challenging environments. Sensors (Basel) 2015;15:13533-47.
    • (2015) Sensors (Basel) , vol.15 , pp. 13533-13547
    • Kjaer, K.H.1    Ottosen, C.-O.2
  • 88
    • 84945333626 scopus 로고    scopus 로고
    • LiDAR: An important tool for next-generation phenotyping technology of high potential for plant phenomics?
    • Lin Y. LiDAR: An important tool for next-generation phenotyping technology of high potential for plant phenomics? Comput Electron Agric 2015;119:61-73.
    • (2015) Comput Electron Agric , vol.119 , pp. 61-73
    • Lin, Y.1
  • 89
    • 84857806920 scopus 로고    scopus 로고
    • Recovery of forest canopy parameters by inversion of multispectral LiDAR data
    • Wallace A, Nichol C, Woodhouse I. Recovery of forest canopy parameters by inversion of multispectral LiDAR data. Remote Sens 2012;4:509-31.
    • (2012) Remote Sens , vol.4 , pp. 509-531
    • Wallace, A.1    Nichol, C.2    Woodhouse, I.3
  • 90
    • 85018260299 scopus 로고    scopus 로고
    • Multispectral LiDAR data for land cover classification of urban areas
    • Morsy S, Shaker A, El-Rabbany A. Multispectral LiDAR data for land cover classification of urban areas. Sensors 2017;17:958.
    • (2017) Sensors , vol.17 , pp. 958
    • Morsy, S.1    Shaker, A.2    El-Rabbany, A.3
  • 91
    • 84896345013 scopus 로고    scopus 로고
    • Design and evaluation of multispectral LiDAR for the recovery of arboreal parameters
    • Wallace AM, McCarthy A, Nichol CJ et al. Design and evaluation of multispectral LiDAR for the recovery of arboreal parameters. IEEE Trans Geosci Remote Sens 2014;52:4942-54.
    • (2014) IEEE Trans Geosci Remote Sens , vol.52 , pp. 4942-4954
    • Wallace, A.M.1    McCarthy, A.2    Nichol, C.J.3
  • 92
    • 85007344299 scopus 로고    scopus 로고
    • A machine learning approach to pedestrian detection for autonomous vehicles using high-definition 3D range data
    • Navarro P, Fernández C, Borraz R et al. A machine learning approach to pedestrian detection for autonomous vehicles using high-definition 3D range data. Sensors 2016;17: 18.
    • (2016) Sensors , vol.17 , pp. 18
    • Navarro, P.1    Fernández, C.2    Borraz, R.3
  • 93
    • 80054085075 scopus 로고    scopus 로고
    • Estimation of soil water deficit in an irrigated cotton field with infrared thermography
    • Padhi J, Misra RK, Payero JO. Estimation of soil water deficit in an irrigated cotton field with infrared thermography. F Crop Res 2012;126:45-55.
    • (2012) F Crop Res , vol.126 , pp. 45-55
    • Padhi, J.1    Misra, R.K.2    Payero, J.O.3
  • 94
    • 53849091663 scopus 로고    scopus 로고
    • On the relationships between stomatal resistance and leaf temperatures in thermography
    • Guilioni L, Jones HG, Leinonen I et al. On the relationships between stomatal resistance and leaf temperatures in thermography. Agric For Meteorol 2008;148:1908-12.
    • (2008) Agric For Meteorol , vol.148 , pp. 1908-1912
    • Guilioni, L.1    Jones, H.G.2    Leinonen, I.3
  • 95
    • 77649244203 scopus 로고    scopus 로고
    • Noninvasive diagnosis of seed viability using infrared thermography
    • Kranner I, Kastberger G, Hartbauer M et al. Noninvasive diagnosis of seed viability using infrared thermography. Proc Natl Acad Sci U S A 2010;107:3912-7.
    • (2010) Proc Natl Acad Sci U S A , vol.107 , pp. 3912-3917
    • Kranner, I.1    Kastberger, G.2    Hartbauer, M.3
  • 96
    • 0036840227 scopus 로고    scopus 로고
    • Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine
    • Jones HG. Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine. J Exp Bot 2002;53:2249-60.
    • (2002) J Exp Bot , vol.53 , pp. 2249-2260
    • Jones, H.G.1
  • 97
    • 84859155416 scopus 로고    scopus 로고
    • Imaging plants dynamics in heterogenic environments
    • Fiorani F, Rascher U, Jahnke S et al. Imaging plants dynamics in heterogenic environments. Curr Opin Biotechnol 2012;23:227-35.
    • (2012) Curr Opin Biotechnol , vol.23 , pp. 227-235
    • Fiorani, F.1    Rascher, U.2    Jahnke, S.3
  • 98
    • 79961175806 scopus 로고    scopus 로고
    • Conserved and divergent rhythms of CAM-related and core clock gene expression in the cactus Opuntia ficus-indica
    • Mallona I, Egea-Cortines M, Weiss J. Conserved and divergent rhythms of CAM-related and core clock gene expression in the cactus Opuntia ficus-indica. Plant Physiol 2011;156:1978-89.
    • (2011) Plant Physiol , vol.156 , pp. 1978-1989
    • Mallona, I.1    Egea-Cortines, M.2    Weiss, J.3
  • 99
    • 0031889304 scopus 로고    scopus 로고
    • The short-period mutant, toc1-1, alters circadian clock regulation of multiple outputs throughout development in Arabidopsis thaliana
    • Somers DE, Webb A A, Pearson M et al. The short-period mutant, toc1-1, alters circadian clock regulation of multiple outputs throughout development in Arabidopsis thaliana. Development 1998;125:485-94.
    • (1998) Development , vol.125 , pp. 485-494
    • Somers, D.E.1    Webb, A.A.2    Pearson, M.3
  • 100
    • 84886887601 scopus 로고    scopus 로고
    • Thermography to explore plant-environment interactions
    • Costa JM, Grant OM, Chaves MM et al. Thermography to explore plant-environment interactions. J Exp Bot 2013;64:3937-49.
    • (2013) J Exp Bot , vol.64 , pp. 3937-3949
    • Costa, J.M.1    Grant, O.M.2    Chaves, M.M.3
  • 101
    • 84875050126 scopus 로고    scopus 로고
    • Infrared thermal imaging as a rapid tool for identifying water-stress tolerant maize genotypes of different phenology
    • Zia S, Romano G, Spreer W et al. Infrared thermal imaging as a rapid tool for identifying water-stress tolerant maize genotypes of different phenology. J Agron Crop Sci 2013;199:75-84.
    • (2013) J Agron Crop Sci , vol.199 , pp. 75-84
    • Zia, S.1    Romano, G.2    Spreer, W.3
  • 102
    • 70449678710 scopus 로고    scopus 로고
    • Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field
    • Jones HG, Serraj R, Loveys BR et al. Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Funct Plant Biol 2009;36:978.
    • (2009) Funct Plant Biol , vol.36 , pp. 978
    • Jones, H.G.1    Serraj, R.2    Loveys, B.R.3
  • 103
    • 0036840227 scopus 로고    scopus 로고
    • Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine
    • Jones HG, Stoll M, Santos T et al. Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine. J Exp Bot 2002;53:2249-60.
    • (2002) J Exp Bot , vol.53 , pp. 2249-2260
    • Jones, H.G.1    Stoll, M.2    Santos, T.3
  • 104
    • 84923635676 scopus 로고    scopus 로고
    • Infra-Red thermography as a highthroughput tool for field phenotyping
    • Prashar A, Jones H. Infra-Red thermography as a highthroughput tool for field phenotyping. Agronomy 2014;4:397-417.
    • (2014) Agronomy , vol.4 , pp. 397-417
    • Prashar, A.1    Jones, H.2
  • 105
    • 84978922953 scopus 로고    scopus 로고
    • Precision agriculture and plant phenotyping are information-and technology-based domains with specific demands and challenges for
    • Mahlein A-K. Precision agriculture and plant phenotyping are information-and technology-based domains with specific demands and challenges for. Plant Dis 2016;100:241-51.
    • (2016) Plant Dis , vol.100 , pp. 241-251
    • Mahlein, A.-K.1
  • 106
    • 80052289471 scopus 로고    scopus 로고
    • Thermographic assessment of scab disease on apple leaves
    • Oerke E-C, Fr öhling P, Steiner U. Thermographic assessment of scab disease on apple leaves. Precis Agric 2011;12:699-715.
    • (2011) Precis Agric , vol.12 , pp. 699-715
    • Oerke, E.-C.1    Fröhling, P.2    Steiner, U.3
  • 107
    • 0026947812 scopus 로고
    • An Arabidopsismutant defective in the general phenylpropanoid pathway
    • Chapple CC, Vogt T, Ellis BE et al. An Arabidopsismutant defective in the general phenylpropanoid pathway. Plant Cell 1992;4:1413-24.
    • (1992) Plant Cell , vol.4 , pp. 1413-1424
    • Chapple, C.C.1    Vogt, T.2    Ellis, B.E.3
  • 108
    • 66249120358 scopus 로고    scopus 로고
    • Delayed fluorescence as a universal tool for the measurement of circadian rhythms in higher plants
    • Gould PD, Diaz P, Hogben C et al. Delayed fluorescence as a universal tool for the measurement of circadian rhythms in higher plants. Plant J 2009;58:893-901.
    • (2009) Plant J , vol.58 , pp. 893-901
    • Gould, P.D.1    Diaz, P.2    Hogben, C.3
  • 109
    • 0018505917 scopus 로고
    • In vivo chlorophylla fluorescence transients and the circadian-rhythm of photosynthesis in gonyaulax-polyedra
    • Sweeney BM, Prezelin BB, Wong D et al. In vivo chlorophylla fluorescence transients and the circadian-rhythm of photosynthesis in gonyaulax-polyedra. Photochem Photobiol 1979;30:309-11.
    • (1979) Photochem Photobiol , vol.30 , pp. 309-311
    • Sweeney, B.M.1    Prezelin, B.B.2    Wong, D.3
  • 110
    • 84961302998 scopus 로고    scopus 로고
    • MYB-FL controls gain and loss of floral UV absorbance, a key trait affecting pollinator preference and reproductive isolation
    • Accessed 30 June 2017.
    • Sheehan H, Moser M, Klahre U et al. MYB-FL controls gain and loss of floral UV absorbance, a key trait affecting pollinator preference and reproductive isolation. Nat Genet 2015. Available at: http://www.nature.com/doifinder/10.1038/ng.3462. Accessed 30 June 2017.
    • (2015) Nat Genet
    • Sheehan, H.1    Moser, M.2    Klahre, U.3
  • 111
    • 85007487806 scopus 로고    scopus 로고
    • Multicolor fluorescence imaging as a candidate for disease detection in plant phenotyping
    • Pérez-Bueno ML, Pineda M, Cabeza FM et al. Multicolor fluorescence imaging as a candidate for disease detection in plant phenotyping. Front Plant Sci 2016;7:1790.
    • (2016) Front Plant Sci , vol.7 , pp. 1790
    • Pérez-Bueno, M.L.1    Pineda, M.2    Cabeza, F.M.3
  • 112
    • 84944314951 scopus 로고    scopus 로고
    • Current and prospective methods for plant disease detection
    • Fang Y, Ramasamy R. Current and prospective methods for plant disease detection. Biosensors 2015;5:537-61.
    • (2015) Biosensors , vol.5 , pp. 537-561
    • Fang, Y.1    Ramasamy, R.2
  • 114
    • 84977465820 scopus 로고    scopus 로고
    • Pathological brain detection based on wavelet entropy and Hu moment invariants
    • Accessed 18 October 2016
    • Zhang Y, Wang S, Sun P. Pathological brain detection based on wavelet entropy and Hu moment invariants. Bio-medical Mater 2015. at: http://content.iospress.com/articles/bio-medical-materials-and-engineering/bme1426. Accessed 18 October 2016.
    • (2015) Bio-medical Mater
    • Zhang, Y.1    Wang, S.2    Sun, P.3
  • 115
    • 84859121620 scopus 로고    scopus 로고
    • Surveying the plant's world by magnetic resonance imaging
    • Accessed 15 May 2017
    • Borisjuk L, Rolletschek H, Neuberger T. Surveying the plant's world by magnetic resonance imaging. Plant J 2012;129-46. Available at: http://doi.wiley.com/10.1111/j.1365-313X.2012.04927.x. Accessed 15 May 2017.
    • (2012) Plant J , pp. 129-146
    • Borisjuk, L.1    Rolletschek, H.2    Neuberger, T.3
  • 116
    • 82955239984 scopus 로고    scopus 로고
    • Non-invasive approaches for phenotyping of enhanced performance traits in bean
    • Rascher U, Blossfeld S, Fiorani F et al. Non-invasive approaches for phenotyping of enhanced performance traits in bean. Funct Plant Biol 2011;38:968-83.
    • (2011) Funct Plant Biol , vol.38 , pp. 968-983
    • Rascher, U.1    Blossfeld, S.2    Fiorani, F.3
  • 117
    • 0035131023 scopus 로고    scopus 로고
    • Visualisation of moisture distribution during development of rice caryopses (Oryza sativa L.) by nuclear magnetic resonance microimaging
    • Accessed 15 May 2017.
    • Horigane A, Engelaar W, Maruyama S. Visualisation of moisture distribution during development of rice caryopses (Oryza sativa L.) by nuclear magnetic resonance microimaging. J Cereal 2001. Available at: http://www.sciencedirect. com/science/article/pii/S0733521000903485. Accessed 15 May 2017.
    • (2001) J Cereal
    • Horigane, A.1    Engelaar, W.2    Maruyama, S.3
  • 118
    • 28444467766 scopus 로고    scopus 로고
    • NMR imaging of developing barley grains
    • Accessed 15 May 2017.
    • Glidewell S. NMR imaging of developing barley grains. J Cereal Sci 2006. Available at: http://www.sciencedirect.com/science/article/pii/S0733521005000913. Accessed 15 May 2017.
    • (2006) J Cereal Sci
    • Glidewell, S.1
  • 119
    • 84880814191 scopus 로고    scopus 로고
    • Positron emission tomography (PET) of radiotracer uptake and distribution in living plants: methodological aspects
    • Converse A, Ahlers E, Bryan T. Positron emission tomography (PET) of radiotracer uptake and distribution in living plants: methodological aspects. J Radioanal Nucl Chem 2013;297(2):241-6.
    • (2013) J Radioanal Nucl Chem , vol.297 , Issue.2 , pp. 241-246
    • Converse, A.1    Ahlers, E.2    Bryan, T.3
  • 120
    • 84946546728 scopus 로고    scopus 로고
    • In vivo quantitative imaging of photoassimilate transport dynamics and allocation in large plants using a commercial positron emission tomography (PET) scanner
    • Karve AA, AlexoffD, Kim D et al. In vivo quantitative imaging of photoassimilate transport dynamics and allocation in large plants using a commercial positron emission tomography (PET) scanner. BMC Plant Biol 2015;15:273.
    • (2015) BMC Plant Biol , vol.15 , pp. 273
    • Karve, A.A.1    Alexoff, D.2    Kim, D.3
  • 121
    • 79952150238 scopus 로고    scopus 로고
    • High-throughput measurement of rice tillers using a conveyor equipped with x-ray computed tomography
    • Yang W, Xu X, Duan L et al. High-throughput measurement of rice tillers using a conveyor equipped with x-ray computed tomography. Rev Sci Instrum 2011;82:25102.
    • (2011) Rev Sci Instrum , vol.82 , pp. 25102
    • Yang, W.1    Xu, X.2    Duan, L.3
  • 122
    • 74249094242 scopus 로고    scopus 로고
    • The X-factor: visualizing undisturbed root architecture in soils using X-ray computed tomography
    • Tracy SR, Roberts JA, Black CR et al. The X-factor: visualizing undisturbed root architecture in soils using X-ray computed tomography. J Exp Bot 2010;61:311-3.
    • (2010) J Exp Bot , vol.61 , pp. 311-313
    • Tracy, S.R.1    Roberts, J.A.2    Black, C.R.3
  • 123
    • 84858004392 scopus 로고    scopus 로고
    • Developing X-ray computed tomography to non-invasively image 3-D root systems architecture in soil
    • Mooney SJ, Pridmore TP, Helliwell J et al. Developing X-ray computed tomography to non-invasively image 3-D root systems architecture in soil. Plant Soil 2012;352: 1-22.
    • (2012) Plant Soil , vol.352 , pp. 1-22
    • Mooney, S.J.1    Pridmore, T.P.2    Helliwell, J.3
  • 124
    • 84928708821 scopus 로고    scopus 로고
    • Direct comparison of MRI and X-ray CT technologies for 3D imaging of root systems in soil: potential and challenges for root trait quantification
    • Metzner R, Eggert A, van Dusschoten D et al. Direct comparison of MRI and X-ray CT technologies for 3D imaging of root systems in soil: potential and challenges for root trait quantification. Plant Methods 2015;11:17.
    • (2015) Plant Methods , vol.11 , pp. 17
    • Metzner, R.1    Eggert, A.2    van Dusschoten, D.3
  • 125
    • 33845371951 scopus 로고    scopus 로고
    • Advances in the acquisition and analysis of CT scan data to isolate a crop root system from the soil medium and quantify root system complexity in 3-D space
    • Lontoc-Roy M, Dutilleul P, Prasher SO et al. Advances in the acquisition and analysis of CT scan data to isolate a crop root system from the soil medium and quantify root system complexity in 3-D space. Geoderma 2006;137:231-41.
    • (2006) Geoderma , vol.137 , pp. 231-241
    • Lontoc-Roy, M.1    Dutilleul, P.2    Prasher, S.O.3
  • 126
    • 33751550463 scopus 로고    scopus 로고
    • Non-destructive visualization and quantification of roots using computed tomography
    • Perret JS, Al-Belushi ME, Deadman M. Non-destructive visualization and quantification of roots using computed tomography. Soil Biol Biochem 2007;39:391-9.
    • (2007) Soil Biol Biochem , vol.39 , pp. 391-399
    • Perret, J.S.1    Al-Belushi, M.E.2    Deadman, M.3
  • 127
    • 84884696281 scopus 로고    scopus 로고
    • Plant tissues in 3D via X-Ray tomography: simple contrasting methods allow high resolution imaging
    • Staedler YM, Masson D, Schönenberger J et al. Plant tissues in 3D via X-Ray tomography: simple contrasting methods allow high resolution imaging. PLoS One 2013;8:e75295.
    • (2013) PLoS One , vol.8
    • Staedler, Y.M.1    Masson, D.2    Schönenberger, J.3
  • 128
    • 77955276941 scopus 로고    scopus 로고
    • Plant structure visualization by high-resolution X-ray computed tomography
    • Dhondt S, Vanhaeren H, Van Loo D et al. Plant structure visualization by high-resolution X-ray computed tomography. Trends Plant Sci 2010;15:419-22.
    • (2010) Trends Plant Sci , vol.15 , pp. 419-422
    • Dhondt, S.1    Vanhaeren, H.2    Van Loo, D.3
  • 129
    • 84959114849 scopus 로고    scopus 로고
    • New frontiers in the threedimensional visualization of plant structure and function
    • Brodersen CR, Roddy AB. New frontiers in the threedimensional visualization of plant structure and function. Am J Bot 2016;103:184-8.
    • (2016) Am J Bot , vol.103 , pp. 184-188
    • Brodersen, C.R.1    Roddy, A.B.2
  • 130
    • 84901912228 scopus 로고    scopus 로고
    • Programming computer vision with pytho
    • In: Andy Oram, Mike Hendrikosn, eds. 1st ed. Sebastopol, CA: O'Reilly Media;, Accessed 15 July 2017
    • Solem JE. Programming computer vision with python. In: Andy Oram, Mike Hendrikosn, eds. Programming Computer Vision with Python. 1st ed. Sebastopol, CA: O'Reilly Media; 2012;264. Available at: http://programming computervision.com/. Accessed 15 July 2017.
    • (2012) Programming Computer Vision with Python , pp. 264
    • Solem, J.E.1
  • 131
    • 84965017167 scopus 로고    scopus 로고
    • Machine learning and computer vision system for phenotype data acquisition and analysis in plants
    • Navarro PJ, Pérez F, Weiss J et al. Machine learning and computer vision system for phenotype data acquisition and analysis in plants. Sensors (Switzerland) 2016;16:641.
    • (2016) Sensors (Switzerland) , vol.16 , pp. 641
    • Navarro, P.J.1    Pérez, F.2    Weiss, J.3
  • 132
    • 84969174961 scopus 로고    scopus 로고
    • A survey of image processing techniques for plant extraction and segmentation in the field
    • Accessed 10 May 2017
    • Hamuda E, Glavin M, Jones E. A survey of image processing techniques for plant extraction and segmentation in the field. Comput Electron Agric 2016;184-99. Available at: http://www.sciencedirect.com/science/article/pii/S016816 9916301557. Accessed 10 May 2017.
    • (2016) Comput Electron Agric , pp. 184-199
    • Hamuda, E.1    Glavin, M.2    Jones, E.3
  • 133
    • 84933498482 scopus 로고    scopus 로고
    • Computer vision metrics: survey, taxonomy, and analysis
    • Scott K, ed. Apress. Accessed September 2017.
    • Krig S. Computer vision metrics: survey, taxonomy, and analysis. In: Scott K, ed. Apress; 2014. Available at: http://www.apress.com/us/book/9781430259299. Accessed September 2017.
    • (2014)
    • Krig, S.1
  • 134
    • 84979231126 scopus 로고    scopus 로고
    • Review of plant identification based on image processing
    • Accessed 10 May 2017.
    • Wang Z, Li H, Zhu Y et al. Review of plant identification based on image processing. Arch Comput Methods Eng 2016. Available at: http://link.springer.com/10.1007/s11831-016-9181-4. Accessed 10 May 2017.
    • (2016) Arch Comput Methods Eng
    • Wang, Z.1    Li, H.2    Zhu, Y.3
  • 135
    • 85003815202 scopus 로고    scopus 로고
    • Indian plant species identification under varying illumination and viewpoint conditions
    • Accessed 9 May 2017
    • Bhagwat R, Dandawate Y. Indian plant species identification under varying illumination and viewpoint conditions. In: 2016 Conference on Advances in Signal Processing. IEEE; 2016;469-73. Available at: http://ieeexplore.ieee.org/document/7746217/. Accessed 9 May 2017.
    • (2016) 2016 Conference on Advances in Signal Processing. IEEE; , pp. 469-473
    • Bhagwat, R.1    Dandawate, Y.2
  • 136
    • 84899465080 scopus 로고    scopus 로고
    • Computer visionimage enhancement for plant leaves disease detectio
    • 2014. IEEE; Accessed 10 May 2017
    • Thangadurai K, Padmavathi K. Computer visionimage enhancement for plant leaves disease detection. In: 2014 World Congress on Computing and Communication Technologies; 2014. IEEE; 2014;173-5. Available at: http://ieeexplore.ieee.org/document/6755131/. Accessed 10 May 2017.
    • (2014) 2014 World Congress on Computing and Communication Technologies , pp. 173-175
    • Thangadurai, K.1    Padmavathi, K.2
  • 137
    • 84960918247 scopus 로고    scopus 로고
    • Implementation of RGB and grayscale images in plant leaves disease detection -comparative study
    • Accessed 20 May 2017
    • Padmavathi K, Thangadurai K. Implementation of RGB and grayscale images in plant leaves disease detection -comparative study. Indian J Sci Technol 2016;9. Available at: http://www.indjst.org/index.php/indjst/article/view/77739. Accessed 20 May 2017.
    • (2016) Indian J Sci Technol , pp. 9
    • Padmavathi, K.1    Thangadurai, K.2
  • 138
    • 84962269370 scopus 로고    scopus 로고
    • The FAIR Guiding Principles for scientific data management and stewardship
    • Wilkinson MD, Dumontier M, Aalbersberg IJJ et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016;3:160018.
    • (2016) Sci Data , vol.3 , pp. 160018
    • Wilkinson, M.D.1    Dumontier, M.2    Aalbersberg, I.J.J.3
  • 139
    • 85016928824 scopus 로고    scopus 로고
    • Detection of plant leaf diseases using image segmentation and soft computing techniques
    • Singh V, MisraAK. Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agric 2017;4:41-9.
    • (2017) Inf Process Agric , vol.4 , pp. 41-49
    • Singh, V.1    Misra, A.K.2
  • 140
    • 84918522479 scopus 로고    scopus 로고
    • ApLeaf: an efficient android-based plant leaf identification system
    • Zhao ZQ, Ma LH, Cheung Y ming et al. ApLeaf: an efficient android-based plant leaf identification system. Neurocomputing 2015;151:1112-9.
    • (2015) Neurocomputing , vol.151 , pp. 1112-1119
    • Zhao, Z.Q.1    Ma, L.H.2    Cheung, Y.3
  • 141
    • 0018306059 scopus 로고
    • A threshold selection method from gray-level histograms
    • Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;9:62-6.
    • (1979) IEEE Trans Syst Man Cybern , vol.9 , pp. 62-66
    • Otsu, N.1
  • 142
    • 84938568740 scopus 로고    scopus 로고
    • Location and image-based plant recognition and recording system
    • Accessed 30 May 2017
    • Liu J-C, Lin T-M. Location and image-based plant recognition and recording system. J Inform Hiding and Multimedia Signal Processing 2015;6(5):898-910. Available at: http://www.jihmsp.org/~jihmsp/2015/vol6/JIH-MSP-2015-05-007.pdf. Accessed 30 May 2017.
    • (2015) J Inform Hiding and Multimedia Signal Processing , vol.6 , Issue.5 , pp. 898-910
    • Liu, J.-C.1    Lin, T.-M.2
  • 143
    • 84964374694 scopus 로고    scopus 로고
    • Shape descriptors to characterize the shoot of entire plant from multiple side views of a motorized depth sensor
    • Accessed 19 May 2017
    • Chéné Y, Rousseau D, Belin É tienn et al. Shape descriptors to characterize the shoot of entire plant from multiple side views of a motorized depth sensor. Mach Vis Appl 2016;1-15. Available at: http://link.springer.com/10.1007/s00138-016-0762-x. Accessed 19 May 2017.
    • (2016) Mach Vis Appl , pp. 1-15
    • Chéné, Y.1    Rousseau, D.2    Belin, E.3
  • 144
    • 0026172104 scopus 로고
    • Watersheds in digital spaces: an efficient algorithm based on immersion simulations
    • Vincent L, Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 1991;13:583-98.
    • (1991) IEEE Trans Pattern Anal Mach Intell , vol.13 , pp. 583-598
    • Vincent, L.1    Vincent, L.2    Soille, P.3
  • 145
    • 85020585815 scopus 로고    scopus 로고
    • Identification of growth rate of plant based on leaf features using digital image processing techniques
    • Patil S, Soma S, Nandyal S. Identification of growth rate of plant based on leaf features using digital image processing techniques. Int J Emerg Technol Adv Eng 2013;3.
    • (2013) Int J Emerg Technol Adv Eng , vol.3
    • Patil, S.1    Soma, S.2    Nandyal, S.3
  • 146
    • 85019351419 scopus 로고    scopus 로고
    • Individual tree crown delineation from airborne laser scanning for diseased larch forest stands
    • Barnes C, Balzter H, Barrett K et al. Individual tree crown delineation from airborne laser scanning for diseased larch forest stands. Remote Sens 2017;9:231.
    • (2017) Remote Sens , vol.9 , pp. 231
    • Barnes, C.1    Balzter, H.2    Barrett, K.3
  • 147
    • 85014893759 scopus 로고    scopus 로고
    • Watershed and supervised classification based fully automated method for separate leaf segmentation
    • Accessed 20 August 2017
    • Vukadinovic D, Polder G. Watershed and supervised classification based fully automated method for separate leaf segmentation. The Netherland Congress on Computer Vision; 2015;1-2. Available at: http://edepot.wur.nl/385470. Accessed 20 August 2017.
    • (2015) The Netherland Congress on Computer Vision; , pp. 1-2
    • Vukadinovic, D.1    Polder, G.2
  • 148
    • 12844262766 scopus 로고    scopus 로고
    • GrabCut-interactive foreground extraction using iterated graph cuts
    • Accessed 10 May 2017.
    • Rother C, Kolmogorov V, Blake A. GrabCut-interactive foreground extraction using iterated graph cuts. ACM Trans Graph 2004. Available at: https://www.microsoft.com/en-us/research/publication/grabcut-interactive-foregroundextraction-using-iterated-graph-cuts/. Accessed 10 May 2017.
    • (2004) ACM Trans Graph
    • Rother, C.1    Kolmogorov, V.2    Blake, A.3
  • 149
    • 0034844730 scopus 로고    scopus 로고
    • Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images
    • Vancouver, Canada, July. IEEE; 2001. Accessed 3 November 2016.
    • Boykov YY, Jolly M-P. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In: Proceedings of "Internation Conference on Computer Vision"; Vancouver, Canada, July 2001. p. 105-12. IEEE; 2001. Available at: http://ieeexplore.ieee.org/document/937505/. Accessed 3 November 2016.
    • (2001) Proceedings of "Internation Conference on Computer Vision" , pp. 105-112
    • Boykov, Y.Y.1    Jolly, M.-P.2
  • 151
    • 85042178664 scopus 로고    scopus 로고
    • The grabcut segmentation technique as used in the study of tree image extraction
    • Zhu FG, X, eds.; Qingdao, China, November 2009. Qingdao, China: Academy Publisher. Accessed 10 September 2017.
    • Wang X. The grabcut segmentation technique as used in the study of tree image extraction. In: Zhu FG, X, eds. In: Proceedings of the 2009 International Workshop on Information Security and Application (IWISA 2009); Qingdao, China, November 2009. Qingdao, China: Academy Publisher; 2009. Available at: https://pdfs. semanticscholar.org/538c/ff4cf1f660e1b5ab8ed115f272b612 2035e3.pdf. Accessed 10 September 2017.
    • (2009) Proceedings of the 2009 International Workshop on Information Security and Application (IWISA 2009)
    • Wang, X.1
  • 154
    • 84905095747 scopus 로고    scopus 로고
    • Image-based plant phenotyping with incremental learning and active contours
    • Minervini M, Abdelsamea MM, Tsaftaris SA. Image-based plant phenotyping with incremental learning and active contours. Ecol Inform 2014;23:35-48.
    • (2014) Ecol Inform , vol.23 , pp. 35-48
    • Minervini, M.1    Abdelsamea, M.M.2    Tsaftaris, S.A.3
  • 156
    • 42649136920 scopus 로고    scopus 로고
    • A real-time algorithm for the approximation of level-set-based curve evolution
    • Shi Y, KarlWC. A real-time algorithm for the approximation of level-set-based curve evolution. IEEE Trans Image Process 2008;17:645-56.
    • (2008) IEEE Trans Image Process , vol.17 , pp. 645-656
    • Shi, Y.1    Karl, W.C.2
  • 158
    • 84989797716 scopus 로고    scopus 로고
    • Utilizing machine learning approaches to improve the prediction of leaf counts and individual leaf segmentation of rosette plant image
    • Accessed 21 September 2016
    • Pape J-M, Klukas C. Utilizing machine learning approaches to improve the prediction of leaf counts and individual leaf segmentation of rosette plant images. In: Computer Vision Problems in Plant Phenotyping (CVPPP 2015). British Machine Vision Association; 2015;1-12. Available at: http://www.bmva.org/bmvc/2015/cvppp/papers/paper003/index.html. Accessed 21 September 2016.
    • (2015) Computer Vision Problems in Plant Phenotyping (CVPPP 2015). British Machine Vision Association; , pp. 1-12
    • Pape, J.-M.1    Klukas, C.2
  • 159
    • 84880150444 scopus 로고    scopus 로고
    • Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features
    • Arivazhagan S, Shebiah RN, Ananthi S et al. Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int CIGR J 2013;15:211-7.
    • (2013) Agric Eng Int CIGR J , vol.15 , pp. 211-217
    • Arivazhagan, S.1    Shebiah, R.N.2    Ananthi, S.3
  • 160
    • 0042066362 scopus 로고    scopus 로고
    • Classification and identification of Arabidopsis cell wall mutants using Fourier-Transform InfraRed (FT-IR) microspectroscopy
    • Mouille G, Robin S, LecomteMet al. Classification and identification of Arabidopsis cell wall mutants using Fourier-Transform InfraRed (FT-IR) microspectroscopy. Plant J 2003;35:393-404.
    • (2003) Plant J , vol.35 , pp. 393-404
    • Mouille, G.1    Robin, S.2    Lecomte, M.3
  • 161
    • 84943813092 scopus 로고    scopus 로고
    • Discrete wavelets transform for improving greenness image segmentation in agricultural images
    • Guijarro M, Riomoros I, Pajares G et al. Discrete wavelets transform for improving greenness image segmentation in agricultural images. Comput Electron Agric 2015;118:396-407.
    • (2015) Comput Electron Agric , vol.118 , pp. 396-407
    • Guijarro, M.1    Riomoros, I.2    Pajares, G.3
  • 162
    • 77949519597 scopus 로고    scopus 로고
    • Imaging and analysis platform for automatic phenotyping and trait ranking of plant root systems
    • Iyer-Pascuzzi AS, Symonova O, Mileyko Y et al. Imaging and analysis platform for automatic phenotyping and trait ranking of plant root systems. Plant Physiol 2010;152:1148-57.
    • (2010) Plant Physiol , vol.152 , pp. 1148-1157
    • Iyer-Pascuzzi, A.S.1    Symonova, O.2    Mileyko, Y.3
  • 163
    • 3042535216 scopus 로고    scopus 로고
    • Distinctive image features from scale-invariant keypoints
    • Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis 2004;60:91-110.
    • (2004) Int J Comput Vis , vol.60 , pp. 91-110
    • Lowe, D.G.1
  • 166
    • 84924917101 scopus 로고    scopus 로고
    • Automated characterization of flowering dynamics in rice using field-acquired timeseries RGB images
    • Guo W, Fukatsu T, Ninomiya S. Automated characterization of flowering dynamics in rice using field-acquired timeseries RGB images. Plant Methods 2015;11:7.
    • (2015) Plant Methods , vol.11 , pp. 7
    • Guo, W.1    Fukatsu, T.2    Ninomiya, S.3
  • 167
    • 84891358232 scopus 로고    scopus 로고
    • Image-based 3D digitizing for plant architecture analysis and phenotyping
    • Accessed 9 December 2016.
    • Santos T, Oliveira A. Image-based 3D digitizing for plant architecture analysis and phenotyping. In: SIBGRAPI 2012 XXV Conf. 2012. Available at: http://www.cnptia.embrapa.br/~thiago/pool/2012-08-24 sibgrapi.pdf. Accessed 9 December 2016.
    • (2012) SIBGRAPI 2012 XXV Conf.
    • Santos, T.1    Oliveira, A.2
  • 168
    • 84890387560 scopus 로고    scopus 로고
    • Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields
    • Accessed 11 March 2017
    • Roscher R, Herzog K, Kunkel A et al. Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields. Comput Electron Agric 2014;100:148-58. Available at: http://www.sciencedirect.com/science/article/pii/S016816991300 2780. Accessed 11 March 2017.
    • (2014) Comput Electron Agric , vol.100 , pp. 148-158
    • Roscher, R.1    Herzog, K.2    Kunkel, A.3
  • 169
    • 84907372351 scopus 로고    scopus 로고
    • Machine Learning With R.
    • 1st ed. Jones J, Sheikh A, eds. Birmingham: Packt Publishing
    • Lantz B. Machine LearningWith R. 1st ed. Jones J, Sheikh A, eds. Birmingham: Packt Publishing; 2013.
    • (2013)
    • Lantz, B.1
  • 172
    • 84926623097 scopus 로고    scopus 로고
    • Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria
    • Baranowski P, Jedryczka M, MazurekWet al. Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria. PLoS One 2015;10:e0122913.
    • (2015) PLoS One , vol.10
    • Baranowski, P.1    Jedryczka, M.2    Mazurek, W.3
  • 173
    • 84944735469 scopus 로고    scopus 로고
    • Boston, USA: MIT Press; Accessed 21 August
    • Goodfellow I, Bengio Y, Courville A. Deep Learning. Boston, USA: MIT Press; Available at: www.deeplearningbook.org. Accessed 21 August 2017.
    • (2017) Deep Learning
    • Goodfellow, I.1    Bengio, Y.2    Courville, A.3
  • 174
    • 0019152630 scopus 로고
    • Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
    • Fukushima K. Neocognitron: a self organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 1980;36:193-202.
    • (1980) Biol Cybern , vol.36 , pp. 193-202
    • Fukushima, K.1
  • 175
    • 85020120388 scopus 로고    scopus 로고
    • Deep machine learning provides state-of-the-art performance in imagebased plant phenotyping
    • Accessed 21 September 2016
    • Pound MP, Burgess AJ, Wilson MH et al. Deep machine learning provides state-of-the-art performance in imagebased plant phenotyping. BioRxiv 2016;53033. Available at: http://biorxiv.org/lookup/doi/10.1101/053033. Accessed 21 September 2016.
    • (2016) BioRxiv
    • Pound, M.P.1    Burgess, A.J.2    Wilson, M.H.3
  • 176
    • 85027839444 scopus 로고    scopus 로고
    • Using deep learning for image-based plant disease detection
    • Mohanty SP, Hughes D, Salathé M. Using deep learning for image-based plant disease detection. 2016;1-7.
    • (2016) , pp. 1-7
    • Mohanty, S.P.1    Hughes, D.2    Salathé, M.3
  • 177
    • 84997343143 scopus 로고    scopus 로고
    • Machine learning for plant phenotyping needs image processing
    • Accessed 27 May 2017
    • Tsaftaris SA, Minervini M, Scharr H. Machine learning for plant phenotyping needs image processing. Trends Plant Sci 2016;989-91. Available at: http://linkinghub.elsevier.com/retrieve/pii/S1360138516301613. Accessed 27 May 2017.
    • (2016) Trends Plant Sci , pp. 989-991
    • Tsaftaris, S.A.1    Minervini, M.2    Scharr, H.3
  • 178
    • 85018485620 scopus 로고    scopus 로고
    • A real-time phenotyping framework using machine learning for plant stress severity rating in soybean
    • Accessed 21 August 2017
    • Naik HS, Zhang J, Lofquist A et al. A real-time phenotyping framework using machine learning for plant stress severity rating in soybean. Plant Methods 2017;13:23. Available at: http://plantmethods.biomedcentral.com/articles/10.1186/s13007-017-0173-7. Accessed 21 August 2017.
    • (2017) Plant Methods , vol.13 , pp. 23
    • Naik, H.S.1    Zhang, J.2    Lofquist, A.3
  • 179
    • 10844264572 scopus 로고    scopus 로고
    • Cellulose absorption index (CAI) to quantifymixed soil-plant litter scenes
    • Nagler PL, Inoue Y, Glenn E. et al. Cellulose absorption index (CAI) to quantifymixed soil-plant litter scenes. Remote Sens Environ 2003;87:310-25.
    • (2003) Remote Sens Environ , vol.87 , pp. 310-325
    • Nagler, P.L.1    Inoue, Y.2    Glenn, E.3
  • 180
    • 84862819045 scopus 로고    scopus 로고
    • Evaluating cellulose absorption index (CAI) for non-photosynthetic biomass estimation in the desert steppe of InnerMongolia
    • Ren H, Zhou G, Zhang F et al. Evaluating cellulose absorption index (CAI) for non-photosynthetic biomass estimation in the desert steppe of InnerMongolia. Chinese Sci Bull 2012;57:1716-22.
    • (2012) Chinese Sci Bull , vol.57 , pp. 1716-1722
    • Ren, H.1    Zhou, G.2    Zhang, F.3
  • 181
    • 56949103056 scopus 로고    scopus 로고
    • Effects of soil composition and mineralogy on remote sensing of crop residue cover
    • Serbin G, Daughtry CST, Hunt ER et al. Effects of soil composition and mineralogy on remote sensing of crop residue cover. Remote Sens Environ 2009;113:224-38.
    • (2009) Remote Sens Environ , vol.113 , pp. 224-238
    • Serbin, G.1    Daughtry, C.S.T.2    Hunt, E.R.3
  • 182
    • 85020170286 scopus 로고    scopus 로고
    • Evaluating spectral indices for determining conservation and conventional tillage systems in a vetch-wheat rotation
    • Eskandari I, Navid H, Rangzan K. Evaluating spectral indices for determining conservation and conventional tillage systems in a vetch-wheat rotation. Int Soil Water Conserv Res 2016;4:93-8.
    • (2016) Int Soil Water Conserv Res , vol.4 , pp. 93-98
    • Eskandari, I.1    Navid, H.2    Rangzan, K.3
  • 183
    • 12944276914 scopus 로고    scopus 로고
    • Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data
    • Galvão LS, Formaggio AR, Tisot DA. Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote Sens Environ 2005;94:523-34.
    • (2005) Remote Sens Environ , vol.94 , pp. 523-534
    • Galvão, L.S.1    Formaggio, A.R.2    Tisot, D.A.3
  • 184
    • 0028979195 scopus 로고
    • Leaf area index estimation from visible and nearinfrared reflectance data
    • Price J. Leaf area index estimation from visible and nearinfrared reflectance data. Remote Sens Environ 1995;52:55-65.
    • (1995) Remote Sens Environ , vol.52 , pp. 55-65
    • Price, J.1
  • 185
    • 33847668883 scopus 로고    scopus 로고
    • Stress detection in crops with hyperspectral remote sensing and physical simulation models
    • Zarco-Tejada P, Berjón A, Miller J. Stress detection in crops with hyperspectral remote sensing and physical simulation models. Proc Airborne 2004.
    • (2004) Proc Airborne
    • Zarco-Tejada, P.1    Berjón, A.2    Miller, J.3
  • 186
    • 84866907246 scopus 로고    scopus 로고
    • Novel image segmentation based on machine learning and its application to plant analysis
    • Cai J, Golzarian MR, Miklavcic SJ. Novel image segmentation based on machine learning and its application to plant analysis. Int J Inf Electron Eng 2011;1.
    • (2011) Int J Inf Electron Eng , vol.1
    • Cai, J.1    Golzarian, M.R.2    Miklavcic, S.J.3
  • 187
    • 0042382986 scopus 로고    scopus 로고
    • Estimation of forest leaf area index using vegetation indices derived fromHyperion hyperspectral data
    • Gong P, Pu R, Biging G. Estimation of forest leaf area index using vegetation indices derived fromHyperion hyperspectral data. IEEE Trans 2003.
    • (2003) IEEE Trans
    • Gong, P.1    Pu, R.2    Biging, G.3
  • 188
    • 41249087111 scopus 로고    scopus 로고
    • Comparison of three satellite sensors at three spatial scales to predict larval mosquito presence in Connecticut wetlands
    • Brown HE, Diuk-Wasser MA, Guan Y et al. Comparison of three satellite sensors at three spatial scales to predict larval mosquito presence in Connecticut wetlands. Remote Sens Environ 2008;112:2301-8.
    • (2008) Remote Sens Environ , vol.112 , pp. 2301-2308
    • Brown, H.E.1    Diuk-Wasser, M.A.2    Guan, Y.3
  • 189
    • 1242265184 scopus 로고    scopus 로고
    • Detecting sugarcane "orange rust" disease using EO-1 Hyperion hyperspectral imagery
    • Apan A, Held A, Phinn S et al. Detecting sugarcane "orange rust" disease using EO-1 Hyperion hyperspectral imagery. Int J Remote Sens 2004;25:489-98.
    • (2004) Int J Remote Sens , vol.25 , pp. 489-498
    • Apan, A.1    Held, A.2    Phinn, S.3
  • 190
    • 0035513550 scopus 로고    scopus 로고
    • Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999
    • Tucker CJ, Slayback DA, Pinzon JE et al. Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. Int J Biometeorol 2001;45:184-90.
    • (2001) Int J Biometeorol , vol.45 , pp. 184-190
    • Tucker, C.J.1    Slayback, D.A.2    Pinzon, J.E.3
  • 191
    • 1842431418 scopus 로고    scopus 로고
    • Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture
    • Haboudane D, Miller JR, Pattey E et al. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ 2004;90: 337-52.
    • (2004) Remote Sens Environ , vol.90 , pp. 337-352
    • Haboudane, D.1    Miller, J.R.2    Pattey, E.3
  • 192
    • 63049107245 scopus 로고    scopus 로고
    • Characterization of corn nitrogen status with a greenness index under different availability of sulfur
    • Pagani A, Echeverría HE, Andrade FH et al. Characterization of corn nitrogen status with a greenness index under different availability of sulfur. Agron J Am Soc Agron 2009;101:315.
    • (2009) Agron J Am Soc Agron , vol.101 , pp. 315
    • Pagani, A.1    Echeverría, H.E.2    Andrade, F.H.3
  • 193
    • 0032030358 scopus 로고    scopus 로고
    • Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves
    • Blackburn GA. Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. Int J Remote Sens 1998;19:657-75.
    • (1998) Int J Remote Sens , vol.19 , pp. 657-675
    • Blackburn, G.A.1
  • 194
    • 77956640482 scopus 로고    scopus 로고
    • Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring
    • Hunt ER, Dean Hively W, Fujikawa SJ et al. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sens 2010;2:290-305.
    • (2010) Remote Sens , vol.2 , pp. 290-305
    • Hunt, E.R.1    Dean Hively, W.2    Fujikawa, S.J.3
  • 195
    • 3142708063 scopus 로고    scopus 로고
    • Optical sensing of turfgrass chlorophyll content and tissue nitrogen
    • Bell GE, Howell BM, Johnson GV et al. Optical sensing of turfgrass chlorophyll content and tissue nitrogen. HortScience 2004;39:1130-2.
    • (2004) HortScience , vol.39 , pp. 1130-1132
    • Bell, G.E.1    Howell, B.M.2    Johnson, G.V.3
  • 196
    • 0036323544 scopus 로고    scopus 로고
    • Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture
    • Haboudane D, Miller JR, Tremblay N et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens Environ 2002;81:416-26.
    • (2002) Remote Sens Environ , vol.81 , pp. 416-426
    • Haboudane, D.1    Miller, J.R.2    Tremblay, N.3
  • 197
    • 84866522698 scopus 로고    scopus 로고
    • Fine-scale remotely-sensed cover mapping of coastal dune and salt marsh ecosystems at Cape Cod National Seashore using Random Forests
    • Timm BC, McGarigal K. Fine-scale remotely-sensed cover mapping of coastal dune and salt marsh ecosystems at Cape Cod National Seashore using Random Forests. Remote Sens Environ 2012;127:106-17.
    • (2012) Remote Sens Environ , vol.127 , pp. 106-117
    • Timm, B.C.1    McGarigal, K.2
  • 198
    • 0242541740 scopus 로고    scopus 로고
    • Characterization of the state of soil degradation by erosion using the hue and coloration indices. IGARSS 2003
    • IGARSS '03. Proceedings. IEEE; 2003
    • Parenteau MP, Bannari A, El-Harti A et al. Characterization of the state of soil degradation by erosion using the hue and coloration indices. IGARSS 2003. In: Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. IEEE; 2003;2284-6.
    • (2003) Geoscience and Remote Sensing Symposium , pp. 2284-2286
    • Parenteau, M.P.1    Bannari, A.2    El-Harti, A.3
  • 199
    • 25144515016 scopus 로고    scopus 로고
    • Performance analysis of homomorphic systems for image change detectio
    • Accessed 25 July 2017. Berlin: Springer;
    • Pajares G, Ruz JJ, de la Cruz JM. Performance analysis of homomorphic systems for image change detection. In: Pattern Recognition. Image Anal. Pt 1. Berlin: Springer; 2005;563-70. Available at: http://link.springer.com/10.1007/11492429 68. Accessed 25 July 2017.
    • (2005) Pattern Recognition. Image Anal. , pp. 563-570
    • Pajares, G.1    Ruz, J.J.2    de la Cruz, J.M.3
  • 200
    • 85000635890 scopus 로고    scopus 로고
    • On the application of genetic probabilistic neural networks and cellular neural networks in precision agriculture
    • Oluleye B, Leisa A, Jinsong L et al. On the application of genetic probabilistic neural networks and cellular neural networks in precision agriculture. Asian J Comput Inf Syst 2014;2:90-101.
    • (2014) Asian J Comput Inf Syst , vol.2 , pp. 90-101
    • Oluleye, B.1    Leisa, A.2    Jinsong, L.3
  • 201
    • 33947614142 scopus 로고    scopus 로고
    • Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features
    • Huang K-Y. Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. Comput Electron Agric 2007;57: 3-11.
    • (2007) Comput Electron Agric , vol.57 , pp. 3-11
    • Huang, K.-Y.1
  • 202
    • 0343238472 scopus 로고    scopus 로고
    • Colour and shape analysis techniques for weed detection in cereal fields
    • Perez AJ, Lopez F, Benlloch J V et al. Colour and shape analysis techniques for weed detection in cereal fields. Comput Electron Agric 2000;25:197-212.
    • (2000) Comput Electron Agric , vol.25 , pp. 197-212
    • Perez, A.J.1    Lopez, F.2    Benlloch, J.V.3
  • 203
    • 84922032594 scopus 로고    scopus 로고
    • Identifying Two of Tomatoes Leaf Viruses Using Support Vector Machine
    • New Delhi: Springer; Accessed 11 September 2017
    • Mokhtar U, Ali MAS, Hassanien AE et al. Identifying Two of Tomatoes Leaf Viruses Using Support Vector Machine. New Delhi: Springer; 2015;771-82. Available at: http://link. springer.com/10.1007/978-81-322-2250-7 77. Accessed 11 September 2017.
    • (2015) , pp. 771-782
    • Mokhtar, U.1    Ali, M.A.S.2    Hassanien, A.E.3
  • 204
    • 84932111493 scopus 로고    scopus 로고
    • Development of a wireless computer vision instrument to detect biotic stress in wheat
    • Casanova J, O'Shaughnessy S, Evett S et al. Development of a wireless computer vision instrument to detect biotic stress in wheat. Sensors 2014;14:17753-69.
    • (2014) Sensors , vol.14 , pp. 17753-17769
    • Casanova, J.1    O'Shaughnessy, S.2    Evett, S.3
  • 205
    • 85030666319 scopus 로고    scopus 로고
    • Modified 3D time-offlight camera for object separation in organic farming
    • Accessed 9 August 2017, In: Kress BC, Osten W, Urbach HP, eds
    • Knoll FJ, Holtorf T, Hussmann S. Modified 3D time-offlight camera for object separation in organic farming. In: Kress BC, Osten W, Urbach HP, eds. International Society for Optics and Photonics; 2017;103351R. Available at: http://proceedings.spiedigitallibrary.org/proceeding.aspx? doi=10.1117/12.2270276. Accessed 9 August 2017.
    • (2017) International Society for Optics and Photonics
    • Knoll, F.J.1    Holtorf, T.2    Hussmann, S.3
  • 206
    • 79955798770 scopus 로고    scopus 로고
    • The potential of automatic methods of classification to identify leaf diseases frommultispectral images
    • Bauer SD, Korč F, Förstner W. The potential of automatic methods of classification to identify leaf diseases frommultispectral images. Precis Agric 2011;12:361-77.
    • (2011) Precis Agric , vol.12 , pp. 361-377
    • Bauer, S.D.1    Korč, F.2    Förstner, W.3
  • 207
    • 85042185531 scopus 로고    scopus 로고
    • Combining semiautomated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies
    • Accessed 8 August 2017.
    • Atkinson JA, Lobet G, Noll M et al. Combining semiautomated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies. BioRxiv 2017. Available at: http://www.biorxiv.org/content/early/2017/06/20/152702. Accessed 8 August 2017.
    • (2017) BioRxiv
    • Atkinson, J.A.1    Lobet, G.2    Noll, M.3
  • 208
    • 84924352955 scopus 로고    scopus 로고
    • Advances in image pre-processing to improve automated 3d reconstruction
    • Accessed 20 July 2017
    • Ballabeni A, Apollonio FI, Gaiani M et al. Advances in image pre-processing to improve automated 3d reconstruction. Int Arch Photogramm Remote Sens Spat Inf Sci ISPRS Arch 2015;315-23. Available at: http://www.int-archphotogramm-remote-sens-spatial-inf-sci.net/XL-5-W4/315/2015/. Accessed 20 July 2017.
    • (2015) Int Arch Photogramm Remote Sens Spat Inf Sci ISPRS Arch , pp. 315-323
    • Ballabeni, A.1    Apollonio, F.I.2    Gaiani, M.3
  • 209
    • 84930359287 scopus 로고    scopus 로고
    • An improvement stereo vision images processing for object distance measurement
    • Tsung-Shiang H. An improvement stereo vision images processing for object distance measurement. Int J Autom Smart Technol 2015;5:85-90.
    • (2015) Int J Autom Smart Technol , vol.5 , pp. 85-90
    • Tsung-Shiang, H.1
  • 210
    • 84891357486 scopus 로고    scopus 로고
    • Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: analysis and comparison
    • Kazmi W, Foix S, Alenyà G et al. Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: analysis and comparison. ISPRS J PhotogrammRemote Sens 2014;88:128-46.
    • (2014) ISPRS J PhotogrammRemote Sens , vol.88 , pp. 128-146
    • Kazmi, W.1    Foix, S.2    Alenyà, G.3
  • 211
    • 84929485542 scopus 로고    scopus 로고
    • Automatic detection of diseased tomato plants using thermal and stereo visible light images
    • Raza SEA, Prince G, Clarkson JP et al. Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS One 2015;10:e0123262.
    • (2015) PLoS One , vol.10
    • Raza, S.E.A.1    Prince, G.2    Clarkson, J.P.3
  • 212
    • 84930208782 scopus 로고    scopus 로고
    • Field phenotyping of grapevine growth using dense stereo reconstruction
    • Klodt M, Herzog K, Töpfer R et al. Field phenotyping of grapevine growth using dense stereo reconstruction. BMC Bioinformatics 2015;16:143.
    • (2015) BMC Bioinformatics , vol.16 , pp. 143
    • Klodt, M.1    Herzog, K.2    Töpfer, R.3
  • 213
    • 84902436236 scopus 로고    scopus 로고
    • Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery
    • Raza SEA, Smith HK, Clarkson GJJ et al. Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery. PLoS One 2014;9:e97612.
    • (2014) PLoS One , vol.9
    • Raza, S.E.A.1    Smith, H.K.2    Clarkson, G.J.J.3
  • 214
    • 85042202924 scopus 로고    scopus 로고
    • High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth
    • Accessed 10 May 2017
    • Zhang X, Huang C, Wu D et al. High-throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth. Plant Physiol 2017;01516. Available at: http://www.plantphysiol.org/lookup/doi/10.1104/pp.16. Accessed 10 May 2017.
    • (2017) Plant Physiol
    • Zhang, X.1    Huang, C.2    Wu, D.3
  • 215
    • 84960423826 scopus 로고    scopus 로고
    • Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants
    • Wahabzada M, Mahlein A-K, Bauckhage C et al. Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants. Sci Rep 2016;6: 22482.
    • (2016) Sci Rep , vol.6 , pp. 22482
    • Wahabzada, M.1    Mahlein, A.-K.2    Bauckhage, C.3
  • 216
    • 85027692340 scopus 로고    scopus 로고
    • High throughput in vivo analysis of plant leaf chemical properties using hyperspectral imaging
    • Pandey P, Ge Y, Stoerger V et al. High throughput in vivo analysis of plant leaf chemical properties using hyperspectral imaging. Front Plant Sci 2017;8:1348.
    • (2017) Front Plant Sci , vol.8 , pp. 1348
    • Pandey, P.1    Ge, Y.2    Stoerger, V.3
  • 217
    • 84921900726 scopus 로고    scopus 로고
    • Metro maps of plant disease dynamics-automated mining of differences using hyperspectral images
    • Wahabzada M, Mahlein AK, Bauckhage C et al. Metro maps of plant disease dynamics-automated mining of differences using hyperspectral images. PLoS One 2015;10:e0116902.
    • (2015) PLoS One , vol.10
    • Wahabzada, M.1    Mahlein, A.K.2    Bauckhage, C.3
  • 219
    • 85042173364 scopus 로고    scopus 로고
    • Plant disease detection by hyperspectral imaging: from the lab to the field
    • Mahlein A-K, Kuska MT, Thomas S et al. Plant disease detection by hyperspectral imaging: from the lab to the field. Adv Anim Biosci 2017;8:238-43.
    • (2017) Adv Anim Biosci , vol.8 , pp. 238-243
    • Mahlein, A.-K.1    Kuska, M.T.2    Thomas, S.3
  • 220
    • 84977639920 scopus 로고    scopus 로고
    • Measuring and modeling apple trees using time-of-flight data for automation of dormant pruning applications
    • 2016 IEEE;, Accessed 4 September 2017
    • Chattopadhyay S, Akbar SA, Elfiky NM et al. Measuring and modeling apple trees using time-of-flight data for automation of dormant pruning applications. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV); 2016 IEEE; 2016;1-9. Available at: http://ieeexplore. ieee.org/document/7477596/. Accessed 4 September 2017.
    • (2016) 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) , pp. 1-9
    • Chattopadhyay, S.1    Akbar, S.A.2    Elfiky, N.M.3
  • 221
    • 84856448240 scopus 로고    scopus 로고
    • On the use of depth camera for 3D phenotyping of entire plants
    • Chéné Y, Rousseau D, Lucidarme P et al. On the use of depth camera for 3D phenotyping of entire plants. Comput Electron Agric 2012;82:122-7.
    • (2012) Comput Electron Agric , vol.82 , pp. 122-127
    • Chéné, Y.1    Rousseau, D.2    Lucidarme, P.3
  • 222
    • 79952423722 scopus 로고    scopus 로고
    • Plant species classification using a 3D LIDAR sensor and machine learnin
    • 2010. IEEE; Accessed 8 August 2017
    • Weiss U, Biber P, Laible S et al. Plant species classification using a 3D LIDAR sensor and machine learning. In: Ninth International Conference on Machine Learning and Applications; 2010. IEEE; 2010;339-45. Available at: http://ieeexplore.ieee.org/document/5708854/. Accessed 8 August 2017.
    • (2010) Ninth International Conference on Machine Learning and Applications , pp. 339-345
    • Weiss, U.1    Biber, P.2    Laible, S.3
  • 223
    • 85025119424 scopus 로고    scopus 로고
    • Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model
    • Liu S, Baret F, Abichou M et al. Estimating wheat green area index from ground-based LiDAR measurement using a 3D canopy structure model. Agric For Meteorol 2017;247: 12-20.
    • (2017) Agric For Meteorol , vol.247 , pp. 12-20
    • Liu, S.1    Baret, F.2    Abichou, M.3
  • 224
    • 84994667381 scopus 로고    scopus 로고
    • Applying terrestrial lidar for evaluation and calibration of airborne lidar-derived shrub biomass estimates in Arctic tundra
    • Greaves HE, Vierling LA, Eitel JUH et al. Applying terrestrial lidar for evaluation and calibration of airborne lidar-derived shrub biomass estimates in Arctic tundra. Remote Sens Lett 2017;8:175-84.
    • (2017) Remote Sens Lett , vol.8 , pp. 175-184
    • Greaves, H.E.1    Vierling, L.A.2    Eitel, J.U.H.3
  • 225
    • 84880913691 scopus 로고    scopus 로고
    • Surface feature based classification of plant organs from 3D laser scanned point clouds for plant phenotyping
    • Paulus S, Dupuis J, Mahlein A-K et al. Surface feature based classification of plant organs from 3D laser scanned point clouds for plant phenotyping. BMC Bioinformatics 2013;14:238.
    • (2013) BMC Bioinformatics , vol.14 , pp. 238
    • Paulus, S.1    Dupuis, J.2    Mahlein, A.-K.3
  • 226
    • 84949309661 scopus 로고    scopus 로고
    • Comparing generalized linear models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile
    • Lopatin J, Dolos K, Hernández HJ et al. Comparing generalized linear models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile. Remote Sens Environ 2016;173:200-10.
    • (2016) Remote Sens Environ , vol.173 , pp. 200-210
    • Lopatin, J.1    Dolos, K.2    Hernández, H.J.3
  • 227
    • 85007432911 scopus 로고    scopus 로고
    • Methodology for high-throughput field phenotyping of canopy temperature using airborne thermography
    • Deery DM, Rebetzke GJ, Jimenez-Berni JA et al. Methodology for high-throughput field phenotyping of canopy temperature using airborne thermography. Front Plant Sci 2016;7:1808.
    • (2016) Front Plant Sci , vol.7 , pp. 1808
    • Deery, D.M.1    Rebetzke, G.J.2    Jimenez-Berni, J.A.3
  • 228
    • 85017498866 scopus 로고    scopus 로고
    • Assessing plant water status in a hedgerow olive orchard from thermography at plant level
    • García-Tejero IF, Hernández A, Padilla-Díaz CM et al. Assessing plant water status in a hedgerow olive orchard from thermography at plant level. Agric Water Manag 2017;188:50-60.
    • (2017) Agric Water Manag , vol.188 , pp. 50-60
    • García-Tejero, I.F.1    Hernández, A.2    Padilla-Díaz, C.M.3
  • 229
    • 84879457559 scopus 로고    scopus 로고
    • High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis
    • Rousseau C, Belin E, Bove E et al. High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis. Plant Methods 2013;9:17.
    • (2013) Plant Methods , vol.9 , pp. 17
    • Rousseau, C.1    Belin, E.2    Bove, E.3
  • 230
    • 85009788169 scopus 로고    scopus 로고
    • Detection of Huanglongbing in Florida using fluorescence imaging spectroscopy and machine-learning methods
    • Wetterich CB, Felipe de Oliveira Neves R, Belasque J et al. Detection of Huanglongbing in Florida using fluorescence imaging spectroscopy and machine-learning methods. Appl Opt 2017;56:15.
    • (2017) Appl Opt , vol.56 , pp. 15
    • Wetterich, C.B.1    Felipe de Oliveira Neves, R.2    Belasque, J.3
  • 231
    • 84555171092 scopus 로고    scopus 로고
    • Nuclear magnetic resonance: a tool for imaging belowground damage caused by Heterodera schachtii and Rhizoctonia solani on sugar beet
    • Hillnhutter C, Sikora RA, Oerke E-C et al. Nuclear magnetic resonance: a tool for imaging belowground damage caused by Heterodera schachtii and Rhizoctonia solani on sugar beet. J Exp Bot 2012;63:319-27.
    • (2012) J Exp Bot , vol.63 , pp. 319-327
    • Hillnhutter, C.1    Sikora, R.A.2    Oerke, E.-C.3
  • 232
    • 79952150238 scopus 로고    scopus 로고
    • High-throughput measurement of rice tillers using a conveyor equipped with x-ray computed tomography
    • Yang W, Xu X, Duan L et al. High-throughput measurement of rice tillers using a conveyor equipped with x-ray computed tomography. Rev Sci Instrum 2011;82:25102.
    • (2011) Rev Sci Instrum , vol.82 , pp. 25102
    • Yang, W.1    Xu, X.2    Duan, L.3
  • 233
    • 84897006612 scopus 로고    scopus 로고
    • Postharvest noninvasive assessment of fresh chestnut (Castanea spp.) internal decay using computer tomography images
    • Donis-González IR, Guyer DE, Fulbright DW et al. Postharvest noninvasive assessment of fresh chestnut (Castanea spp.) internal decay using computer tomography images. Postharvest Biol Technol 2014;94:14-25.
    • (2014) Postharvest Biol Technol , vol.94 , pp. 14-25
    • Donis-González, I.R.1    Guyer, D.E.2    Fulbright, D.W.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.