Advertisement

New Forests

pp 1–12 | Cite as

Relationships between reflectance and absorbance chlorophyll indices with RGB (Red, Green, Blue) image components in seedlings of tropical tree species at nursery stage

  • Elizabeth Santos do Amaral
  • Daniela Vieira Silva
  • Letícia Dos Anjos
  • Ana Cristina Schilling
  • Ândrea Carla Dalmolin
  • Marcelo Schramm Mielke
Article

Abstract

Methods based on RGB (Red, Green, Blue) image segmentation may emerge as a new and low-cost method for estimation the quality of tree seedlings. However, the vast number of indexes based on the use of the RGB image segmentation and the lack of references in the literature still hinder the widespread use of this technology. Thus, we conducted a study aiming to test the relationships between methods based on absorbance and reflectance, widely used for the estimation of chlorophyll contents and physiological status of trees, and ten indexes based on RGB component analysis. We used leaves of five tropical tree species, belonging to different botanical families. Leaf absorbance was measured using the handheld chlorophyll meter SPAD-502, reflectance was measured using a spectrometer and the RGB indices were obtained from digitalized images of the leaves using a flatbed scanner. Modified linear regression models including all five species were used to relate RGB indices to absorbance and reflectance indices. Data collected from leaves of seedlings of five tropical tree species indicated that digital image processing technology can be a useful and rapid nondestructive method for assessment of physiological status of tree seedlings at nursery stage. Among the RGB indexes tested in this study the R, 2R*(G − B)/(G + B) and 2G*(G − B)/(G + B) are the most promising for analysis the tropical seedlings physiological status and quality.

Keywords

Leaf image analysis Leaf reflectance Multispecies regression model Portable chlorophyll meters 

Notes

Acknowledgements

The authors thank Gerson J. Sales Neto, Nilson A. dos Santos and Rones F. Souza, of Floresta Viva Institute, and M.Sc. Murilo F. C. de Jesus for assistance with data collection. We thank Dr. Fábio P. Gomes of DCB/UESC for providing the SPAD-502 used in this study. Funding for Elizabeth S. Amaral during this study was provided by a scholarship from Capes (Brazilian Higher Education Council). Marcelo S. Mielke gratefully acknowledge CNPq (Brazilian National Council for Scientific and Technological Development) for the award of fellowship of scientific productivity (306531/2015-1). This study was supported by CNPq (561933/2010).

References

  1. Abramoff MD, Magalhaes PJ, Ram SJ (2004) Image processing with ImageJ. Biophoton Int 11:36–42Google Scholar
  2. Adamsen FJ, Pinter PJ, Barnes EM, LaMorte RL, Wall GW, Leavitt SW, Kimball BA (1999) Measuring wheat senescence with a digital camera. Crop Sci 39:719–724CrossRefGoogle Scholar
  3. Ali MM, Al-Ani A, Eamus D, Tan DKY (2012) A new image processing based technique to determine chlorophyll in plants. Am Eurasian J Agric Environ Sci 12:1323–1328Google Scholar
  4. Blackburn GA (2007) Hyperspectral remote sensing of plant pigments. J Exp Bot 58:855–867CrossRefPubMedGoogle Scholar
  5. Box GEP, Cox DR (1964) An analysis of transformations. J R Stat Soc 26:211–252Google Scholar
  6. Calmon M, Brancalion PHS, Paese A, Aronson J, Castro P, Silva SC, Rodrigues RR (2011) Emerging threats and opportunities for large-scale ecological restoration in the Atlantic Forest of Brazil. Restor Ecol 19:154–158CrossRefGoogle Scholar
  7. Campoe OC, Iannelli C, Stape JL, Cook RL, Mendes JCT, Vivian R (2014) Atlantic forest tree species responses to silvicultural practices in a degraded pasture restoration plantation: from leaf physiology to survival and initial growth. For Ecol Manag 313:233–242CrossRefGoogle Scholar
  8. Corbin JD, Holl KD (2012) Applied nucleation as a forest restoration strategy. For Ecol Manag 265:37–46CrossRefGoogle Scholar
  9. Coste S, Baraloto C, Leroy C, Marcon E, Renaud A, Richardson AD, Roggy JC, Schimann H, Uddling J, Hérault B (2010) Assessing foliar chlorophyll contents with the SPAD-502 chlorophyll meter: a calibration test with thirteen tree species of tropical rainforest in French Guiana. Ann For Sci 67:303–310CrossRefGoogle Scholar
  10. Dey AK, Sharma M, Meshram MR (2016) An analysis of leaf chlorophyll measurement method using chlorophyll meter and image processing technique. Procedia Comput Sci 85:286–292CrossRefGoogle Scholar
  11. Dickson A, Leaf AL, Hosner JF (1960) Quality appraisal of white spruce and white pine seedling stock in nurseries. For Chron 36:10–13CrossRefGoogle Scholar
  12. dos Anjos L, Oliva MA, Kuki KN (2012) Fluorescence imaging of light acclimation of brazilian atlantic forest tree species. Photosynthetica 50:95–108CrossRefGoogle Scholar
  13. dos Anjos L, Oliva MA, Kuki KN, Mielke MS, Ventrella MC, Galvão MF, Pinto LRM (2015) Key leaf traits indicative of photosynthetic plasticity in tropical tree species. Trees 29:247–258CrossRefGoogle Scholar
  14. Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall, New YorkCrossRefGoogle Scholar
  15. Gamon JA, Surfus JS (1999) Assessing leaf pigment content and activity with a reflectometer. New Phytol 143:105–117CrossRefGoogle Scholar
  16. Giam X (2017) Global biodiversity loss from tropical deforestation. PNAS 114:5775–5777CrossRefPubMedGoogle Scholar
  17. Gitelson AA, Gritz Y, Merzlyak MN (2003) Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol 160:271–282CrossRefPubMedGoogle Scholar
  18. Gong A, Yu J, He Y, Qiu Z (2013) Citrus yield estimation based on images processed by an Android mobile phone. Biosyst Eng 115:162–170CrossRefGoogle Scholar
  19. Grossnickle SC (2012) Why seedlings survive: influence of plant attributes. New For 43:711–738CrossRefGoogle Scholar
  20. Holl KD, Zahawi RA, Cole RJ, Ostertag R, Cordell S (2011) Planting seedlings in tree islands versus plantations as a large-scale tropical forest restoration strategy. Restor Ecol 19:470–479CrossRefGoogle Scholar
  21. Hu H, Liu H, Zhang H, Zhu J, Yao X, Zhang X, Zheng K (2010) Assessment of chlorophyll content based on image color analysis, comparison with SPAD-502. In: 2010 2nd international conference on information engineering and computer science, pp 1–3Google Scholar
  22. Junker LA, Ensminger I (2016) Relationship between leaf optical properties, chlorophyll fluorescence and pigment changes in senescing Acer saccharum leaves. Tree Physiol 36:694–711CrossRefPubMedGoogle Scholar
  23. Kawashima S, Nakatani M (1998) An algorithm for estimating chlorophyll content in leaves using a video camera. Ann Bot 81:49–54CrossRefGoogle Scholar
  24. Lamb D (2002) Forest restoration—the third big silvicultural challenge. J Trop For Sci 24:295–299Google Scholar
  25. Lee D (2007) Nature’s palette. The science of plant color. The University of Chicago Press, ChicagoCrossRefGoogle Scholar
  26. Li Y, Chen D, Walker CN, Angus JF (2010) Estimating the nitrogen status of crops using a digital camera. Field Crops Res 118:221–227CrossRefGoogle Scholar
  27. Liu Y, Bai SL, Zhu Y, Li GL, Jiang P (2012) Promoting seedling stress resistance through nursery techniques in China. New For 43:639–649CrossRefGoogle Scholar
  28. Maindonald J, Braun J (2003) Data analysis and graphics using R—an example-based approach. Cambridge University Press, CambridgeGoogle Scholar
  29. Markwell J, Osterman JC, Mitchell JL (1995) Calibration of the Minolta SPAD-502 leaf chlorophyll meter. Photosynth Res 46:467–472CrossRefPubMedGoogle Scholar
  30. Mattsson A (1996) Predicting field performance using seedling quality assessment. New For 13:223–248Google Scholar
  31. Mercado-Luna A, Rico-García E, Lara-Herrera A, Soto-Zarazúa G, Ocampo-Velázquez R, Guevara-González R, Herrera-Ruiz R, Torres-Pacheco I (2010) Nitrogen determination on tomato (Lycopersicon esculentum Mill.) seedlings by colour image analysis (RGB). Afr J Biotechnol 33:5326–5332Google Scholar
  32. Mexal JG, Cuevas RRA, Negreros-Castillo P, Paraguirre LC (2002) Nursery production practices affect survival and growth of tropical hardwoods in Quintana Roo, México. For Ecol Manag 168:125–133CrossRefGoogle Scholar
  33. Mielke MS, Schaffer B, Li C (2010) Use of a SPAD meter to estimate chlorophyll content in Eugenia uniflora L. leaves as affected by contrasting light environments and soil flooding. Photosynthetica 48:332–338CrossRefGoogle Scholar
  34. Mielke MS, Schaffer B, Schilling AC (2012) Evaluation of reflectance spectroscopy indices for estimation of chlorophyll content in leaves of a tropical tree species. Photosynthetica 50:343–352CrossRefGoogle Scholar
  35. Murakami PF, Turner MR, van den Berg AK, Schaberg PG (2005) An instructional guide for leaf color analysis using digital imaging software. General Technical Report NE-327. U.S. Department of Agriculture, Forest Service, Northeastern Research Station, Newtown Square, PAGoogle Scholar
  36. Naidu SL, DeLucia EH (1998) Physiological and morphological acclimation of shade-grown tree seedlings to late-season canopy gap formation. Plant Ecol 138:27–40CrossRefGoogle Scholar
  37. Naramoto M, Katahata S, Mukai Y, Kakubari Y (2006) Photosynthetic acclimation and photoinhibition on exposure to high light in shade-developed leaves of Fagus crenata seedlings. Flora 201:120–126CrossRefGoogle Scholar
  38. Nobel P (2009) Physicochemical and environmental plant physiology. Academic Press, New YorkGoogle Scholar
  39. Putra BTW, Soni P (2018) Enhanced broadhand greenness in assessing chlorophyll a and b, carotenoid, and nitrogen in robusta coffee plantations using a digital camera. Precis Agric 19:238–256CrossRefGoogle Scholar
  40. R Development Core Team (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  41. Riccardi M, Miele G, Pulvento C, Lavini A, D’Andria R, Jacobsen SE (2014) Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components. Photosynth Res 120:263–272CrossRefPubMedGoogle Scholar
  42. Richardson AD, Duigan SP, Berlyn GP (2002) An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytol 153:185–194CrossRefGoogle Scholar
  43. Rigon JPG, Capuani S, Fernandes DM, Guimarães TM (2016) A novel method for the estimation of soybean chlorophyll content using a smartphone and image analysis. Photosynthetica 54:559–566CrossRefGoogle Scholar
  44. Rodrigues RR, Lima RAF, Gandolfi S, Nave AG (2009) On the restoration of high diversity forests: 30 years of experiences in the Brazilian Atlantic Forest. Biol Conserv 142:1242–1251CrossRefGoogle Scholar
  45. 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 Sens Environ 81:337–354CrossRefGoogle Scholar
  46. Sokal RR, Rohlf FJ (1995) Biometry: the principles and practice of statistics in biological research. WH Freeman & Comp, New YorkGoogle Scholar
  47. Steele MR, Gitelson AA, Rundquist DCA (2008) Comparison of two techniques for nondestructive measurement of chlorophyll content in grapevine leaves. Agron J 100:87–92CrossRefGoogle Scholar
  48. Torres Netto AT, Campostrini E, Oliveira JG, Smith REB (2005) Photosynthetic pigments, nitrogen, chlorophyll a fluorescence and SPAD-502 readings in coffee leaves. Sci Hortic 104:199–209CrossRefGoogle Scholar
  49. Tsakaldimi M, Ganatsas P, Jacobs DF (2013) Prediction of planted seedling survival of five Mediterranean species based on initial seedling morphology. New For 44:327–339CrossRefGoogle Scholar
  50. Uddling J, Gelang-Alfredsson J, Piikki K, Pleijel H (2007) Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynth Res 91:37–46CrossRefPubMedGoogle Scholar
  51. Van den Berg AK, Perkins TD (2004) Evaluation of a portable chlorophyll meter to estimate chlorophyll and nitrogen contents in sugar maple (Acer saccharum Marsh.) leaves. For Ecol Manag 200:113–117CrossRefGoogle Scholar
  52. Venables WN, Ripley BD (2002) Modern applied statistics with S. Springer, New YorkCrossRefGoogle Scholar
  53. Vibhute A, Bodhe SK (2013) Color image processing approach for nitrogen estimation of vineyard. IJASR 3:189–196Google Scholar
  54. Vieira Silva D, Dos Anjos L, Brito-Rocha E, Dalmolin AC, Mielke MS (2016) Calibration of a multi-species model for chlorophyll estimation in seedlings of Neotropical tree species using hand-held leaf absorbance meters and spectral reflectance. iForest 9:829–834CrossRefGoogle Scholar
  55. Villalobos EB, Cetina VMA, López MAL, Aldrete A, Paniagua DHV (2014) Nursery practices increase seedling performance on nutrient-poor soils in Swietenia humilis. iForest 8:552–557CrossRefGoogle Scholar
  56. Villar-Salvador P, Puértolas J, Cuesta B, Peñuelas JL, Uscola M, Heredia-Guerrero N, Rey BJM (2012) Increase in size and nitrogen concentration enhances seedling survival in Mediterranean plantations. Insights from an ecophysiological conceptual model of plant survival. New For 43:755–770CrossRefGoogle Scholar
  57. Vollmann J, Walter H, Sato T, Schweiger P (2011) Digital image analysis and chlorophyll metering for phenotyping the effects of nodulation in soybean. Comput Electron Agric 75:190–195CrossRefGoogle Scholar
  58. Wang Y, Wang D, Shi P, Omasa K (2014) Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light. Plant Methods 10:1–11CrossRefGoogle Scholar
  59. Wellburn AR (1994) The spectral determination of chlorophylls a and b, as well as total carotenoids, using various solvents with spectrophotometers of different resolution. J Plant Physiol 144:307–313CrossRefGoogle Scholar
  60. Wright SJ (2005) Tropical forests in a changing environment. Trends Ecol Evol 20:553–560CrossRefPubMedGoogle Scholar
  61. Esen D, Yildiz O, Esen U, Edis S, Çetintas C (2012) Effects of cultural treatments, seedling type and morphological characteristics on survival and growth of wild cherry seedlings in Turkey. iForest 5:283–289CrossRefGoogle Scholar
  62. Yadav SP, Ibaraki Y, Dutta Gupta S (2010) Estimation of the chlorophyll content of micropropagated potato plants using RGB based image analysis. Plant Cell, Tissue Organ Cult 100:183–188CrossRefGoogle Scholar
  63. Yuzhu H, Xiaomei W, Shuyao S (2011) Nitrogen determination in pepper (Capsicum frutescens L.) plants by color image analysis (RGB). Afr J Biotechnol 10:17737–17741Google Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Departamento de Ciências BiológicasUniversidade Estadual de Santa CruzIlhéusBrazil
  2. 2.Departamento de Ciências Agrárias e AmbientaisUniversidade Estadual de Santa CruzIlhéusBrazil
  3. 3.Departamento de Bioquímica e Biologia MolecularUniversidade Federal do CearáFortalezaBrazil
  4. 4.Departamento de Ciências Exatas e TecnológicasUniversidade Estadual de Santa CruzIlhéusBrazil

Personalised recommendations