Specalyzer-An interactive online tool to analyze spectral reflectance measurements

PeerJ

Volumn 2018, Issue 6, 2018, Pages

  Koc, Alexander ^a   Henriksson, Tina ^b   Chawade, Aakash ^a  

^a SWEDISH UNIVERSITY OF AGRICULTURAL SCIENCES   (Sweden)

^b Lantmännen Lantbruk   (Sweden)

Author keywords

Online tool; Proximal phenotyping; Specalyzer; Spectral reflectance; Spectroradiometer; Vegetation indices

Indexed keywords

ARTICLE; HIGH THROUGHPUT SEQUENCING; HUMAN; MACHINE LEARNING; PHENOTYPE; PLANT BREEDING; QUALITY CONTROL; REFLECTOMETRY; SOFTWARE; SPATIAL ANALYSIS;

EID: 85049242113     PISSN: None     EISSN: 21678359     Source Type: Journal    
DOI: 10.7717/peerj.5031     Document Type: Article

References (42)

1. Agapiou A, Hadjimitsis D, Alexakis D. 2012. Evaluation of broadband and narrowband vegetation indices for the identification of archaeological crop marks. Remote Sensing 4:3892-3919 DOI 10.3390/rs4123892
2. Attali D. 2018. shinyjs: easily improve the user experience of your shiny apps in seconds. R package version 1.0 ed. Available at https://cran.r-project.org/web/packages/shinyjs/(accessed on 11 June 2018)
3. Babar MA, Reynolds MP, Van Ginkel M, Klatt AR, Raun WR, Stone ML. 2006. Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Science 46:578-588 DOI 10.2135/cropsci2005.0059
4. Calatroni A, Wildfire JJ. 2017. Graphic depiction of bioinformatics data. Journal of Allergy and Clinical Immunology 140:1519-1522 DOI 10.1016/j.jaci.2017.05.043
5. Cao X, Luo Y, Zhou Y, Fan J, Xu X, West JS, Duan X, Cheng D. 2015. Detection of powdery mildew in two winter wheat plant densities and prediction of grain yield using canopy hyperspectral reflectance. PLOS ONE 10(3):1-14 DOI 10.1371/journal.pone.0121462
6. Chang W, Joe C, Allaire JJ, Xie Y, McPherson J. 2017. Shiny: web application framework for R. Available at https://cran.r-project.org/web/packages/shiny/(accessed on 11 June 2018)
7. Chawade A, Alexandersson E, Levander F. 2014. Normalyzer: a tool for rapid evaluation of normalization methods for omics data sets. Journal of Proteome Research 13:3114-3120 DOI 10.1021/pr401264n
8. Chawade A, Armoniené R, Berg G, Brazauskas G, Frostgård G, Geleta M, Gorash A, Henriksson T, Himanen K, Ingver A, Johansson E, Jørgensen LN, Koppel M, Koppel R, Makela P, Ortiz R, Podyma W, Roitsch T, Ronis A, Svensson JT, Vallenback P, Weih M. 2018. A transnational and holistic breeding approach is needed for sustainable wheat production in the Baltic Sea region. Physiologia Plantarum DOI 10.1111/ppl.12726
9. Deery D, Jimenez-Berni J, Jones H, Sirault X, Furbank R. 2014. Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 4:349-379 DOI 10.3390/agronomy4030349
10. Franke J, Menz G. 2007. Multi-temporal wheat disease detection by multi-spectral remote sensing. Precision Agriculture 8:161-172 DOI 10.1007/s11119-007-9036-y
11. Furbank RT, Tester M. 2011. Phenomics-technologies to relieve the phenotyping bottleneck. Trends in Plant Science 16:635-644 DOI 10.1016/j.tplants.2011.09.005
12. Gizaw SA, Garland-Campbell K, Carter AH. 2016. Use of spectral reflectance for indirect selection of yield potential and stability in Pacific Northwest winter wheat. Field Crops Research 196:199-206 DOI 10.1016/j.fcr.2016.06.022
13. Haghighattalab A, González Pérez L, Mondal S, Singh D, Schinstock D, Rutkoski J, Ortiz-Monasterio I, Singh RP, Goodin D, Poland J. 2016. Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods 12(35):1-15 DOI 10.1186/s13007-016-0134-6
14. Hansen PM, Schjoerring JK. 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment 86:542-553 DOI 10.1016/s0034-4257(03)00131-7
15. Kerren A, Kucher K, Li Y-F, Schreiber F. 2017. BioVis explorer: a visual guide for biological data visualization techniques. PLOS ONE 12(11):1-14 DOI 10.1371/journal.pone.0187341
16. Muñoz Huerta R, Guevara-Gonzalez R, Contreras-Medina L, Torres-Pacheco I, Prado-Olivarez J, Ocampo-Velazquez R. 2013. A review of methods for sensing the nitrogen status in plants: advantages, disadvantages and recent advances. Sensors 13:10823-10843 DOI 10.3390/s130810823
17. Lehnert LW, Meyer H, Bendix J. 2017. Hsdar: manage, analyse and simulate hyperspectral data in R. R package version 0.5.1. Available at https://cran.r-project.org/web/packages/hsdar/(accessed on 11 June 2018)
18. Mahlein A-K. 2016. Plant disease detection by imaging sensors-parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease 100:241-251 DOI 10.1094/pdis-03-15-0340-fe
19. Maia R, Eliason CM, Bitton P-P, Doucet SM, Shawkey MD, Tatem A. 2013. pavo: an R package for the analysis, visualization and organization of spectral data. Methods in Ecology and Evolution 4:906-913 DOI 10.1111/2041-210x.12069
20. Odilbekov F, Armoniené R, Henriksson T, Chawade A. 2018. Proximal phenotyping and machine learning methods to identify septoria tritici blotch disease symptoms in wheat. Frontiers in Plant Science 9:1-11 DOI 10.3389/fpls.2018.00685
21. O'Donoghue SI, Gavin A-C, Gehlenborg N, Goodsell DS, Hériché J-K, Nielsen CB, North C, Olson AJ, Procter JB, Shattuck DW, Walter T, Wong B. 2010. Visualizing biological data-now and in the future. Nature Methods 7:S2-S4 DOI 10.1038/nmeth.f.301
22. Pavelin K, Cham JA, De Matos P, Brooksbank C, Cameron G, Steinbeck C. 2012. Bioinformatics meets user-centred design: a perspective. PLOS Computational Biology 8(7):e1002554 DOI 10.1371/journal.pcbi.1002554
23. Peñuelas J, Filella I, Biel C, Serrano L, Savé R. 1993. The reflectance at the 950-970 nm region as an indicator of plant water status. International Journal of Remote Sensing 14:1887-1905 DOI 10.1080/01431169308954010
24. Peñuelas J, Filella I, Gamon JA. 1995. Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytologist 131:291-296 DOI 10.1111/j.1469-8137.1995.tb03064.x
25. R Development Core Team. 2016. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing
26. Roudier P. 2017. asdreader: reading ASD binary files in R. R package version 0.1-3 ed. Available at https://cran.r-project.org/web/packages/asdreader/(accessed on 11 June 2018)
27. Shannon P. 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research 13:2498-2504 DOI 10.1101/gr.1239303
28. Shneiderman B. 1996. The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings 1996 IEEE symposium on visual languages. 336-343
29. Sievert C, Parmer C, Hocking T, Chamberlain S, Ram K, Corvellec M, Despouy P. 2017. plotly: create interactive web graphics via 'plotly.js'. R package version 4.7.1 ed
30. Sims DA, Gamon JA. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment 81:337-354 DOI 10.1016/s0034-4257(02)00010-x
31. Singh A, Ganapathysubramanian B, Singh AK, Sarkar S. 2016. Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science 21:110-124 DOI 10.1016/j.tplants.2015.10.015
32. Tattaris M, Reynolds MP, Chapman SC. 2016. A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Frontiers in Plant Science 7:1-9 DOI 10.3389/fpls.2016.01131
33. Tucker CJ, Sellers PJ. 1986. Satellite remote sensing of primary production. International Journal of Remote Sensing 7:1395-1416 DOI 10.1080/01431168608948944
34. Wang F-M, Huang J-F, Tang Y-L, Wang X-Z. 2007. New vegetation index and its application in estimating leaf area index of rice. Rice Science 14:195-203 DOI 10.1016/s1672-6308(07)60027-4
35. Wickham H. 2007. Reshaping data with the reshape package. Journal of Statistical Software 21(12):1-20 DOI 10.18637/jss.v021.i12
36. Wickham H, Francois R, Henry L, Müller K. 2017a. dplyr: a grammar of data manipulation. R package version 0.7.4 ed. Available at https://cran.r-project.org/web/packages/dplyr/(accessed on 11 June 2018)
37. Wickham H, Hester J, Francois R. 2017b. readr: read rectangular text data. R package version 1.1.1 ed. Available at https://cran.r-project.org/web/packages/readr/(accessed on 11 June 2018)
38. Xue L-H, Cao W-X, Yang L-Z. 2007. Predicting grain yield and protein content in winter wheat at different N supply levels using canopy reflectance spectra. Pedosphere 17:646-653 DOI 10.1016/s1002-0160(07)60077-0
39. Yao X-F, Yao X, Tian Y-C, Ni J, Liu X-J, Cao W-X, Zhu Y. 2013. A new method to determine central wavelength and optimal bandwidth for predicting plant nitrogen uptake in winter wheat. Journal of Integrative Agriculture 12:788-802 DOI 10.1016/s2095-3119(13)60300-7
40. Yau N. 2013a. Data points: visualizations that mean something. Indianapolis: John Wiley & Sons, Inc
41. Yau N. 2013b. Visualizing with clarity. In: Long C, ed. Data points: visualization that means something. Indianapolis: John Wiley & Sons, Inc., 201-240
42. Zhu Z, Hoon HB, Teow K-L. 2015. Interactive data visualization techniques applied to healthcare and decision making. In: Big data analytics in bioinformatics and healthcare. Pennsylvania: IGI global, 46-59 DOI 10.4018/978-1-4666-6611-5.ch003