Photosynthesis Research

, Volume 120, Issue 3, pp 263–272 | Cite as

Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components

  • M. Riccardi
  • G. Mele
  • C. Pulvento
  • A. Lavini
  • R. d’Andria
  • S. -E. Jacobsen
Regular Paper


Leaf chlorophyll content provides valuable information about physiological status of plants; it is directly linked to photosynthetic potential and primary production. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time consuming. Over the years alternative methods, rapid and non-destructive, have been explored. The aim of this work was to evaluate the applicability of a fast and non-invasive field method for estimation of chlorophyll content in quinoa and amaranth leaves based on RGB components analysis of digital images acquired with a standard SLR camera. Digital images of leaves from different genotypes of quinoa and amaranth were acquired directly in the field. Mean values of each RGB component were evaluated via image analysis software and correlated to leaf chlorophyll provided by standard laboratory procedure. Single and multiple regression models using RGB color components as independent variables have been tested and validated. The performance of the proposed method was compared to that of the widely used non-destructive SPAD method. Sensitivity of the best regression models for different genotypes of quinoa and amaranth was also checked. Color data acquisition of the leaves in the field with a digital camera was quick, more effective, and lower cost than SPAD. The proposed RGB models provided better correlation (highest R2) and prediction (lowest RMSEP) of the true value of foliar chlorophyll content and had a lower amount of noise in the whole range of chlorophyll studied compared with SPAD and other leaf image processing based models when applied to quinoa and amaranth.


Leaf image analysis RGB multi-regression model Chenopodium quinoa Amaranthus sp. 


  1. 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
  2. Adolf VI, Jacobsen S-E, Shabala S (2013) Salt tolerance mechanisms in quinoa (Chenopodium quinoa Willd.). Environ Exp Bot 92:43–54CrossRefGoogle 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(10):1323–1328Google Scholar
  4. Araus JL, Amaro T, Voltas J, Nakkoul H, Nachit MM (1998) Chlorophyll fluorescence as a selection criterion for grain yield in durum wheat under Mediterranean conditions. Field Crops Res 55:209–223CrossRefGoogle Scholar
  5. Ashraf M, Harris PJC (2013) Photosynthesis under stressful environments: an overview. Photosynthetica 51(2):163–190CrossRefGoogle Scholar
  6. Bindi M, Hacour A, Vandermeiren K, Craigon J, Ojanpera K, Sellden G, Hogy P, Finnan J, Fibbi L (2002) Chlorophyll concentration of potatoes grown under elevated carbon dioxide and/or ozone concentrations. Eur J Agron 17:319–335CrossRefGoogle Scholar
  7. Bohren CF (2006) Fundamentals of atmospheric radiation: an introduction with 400 problems. Wiley, Chichester. ISBN 978-3-527-40503-9CrossRefGoogle Scholar
  8. Brosnan T, Sun DW (2004) Improving quality inspection of food products by computer vision: a review. J Food Eng 61:3–16CrossRefGoogle Scholar
  9. Cai H, Haixin C, Weitang S, Lihong G (2006) Preliminary study on photosynthetic pigment content and colour feature of cucumber initial blooms. Trans CSAE 22:34–38Google Scholar
  10. Castelli F, Contillo R, Miceli F (1996) Non-destructive determination of leaf chlorophyll content in four crop species. J Agron Crop Sci 177:275–283CrossRefGoogle Scholar
  11. Cenkey S, Cigerci IH, Yýldýz M, Ozay C, Bozdag A, Terzi H (2010) Lead contamination reduces chlorophyll biosynthesis and genomic template stability in Brassica rapa L. Environ Exp Bot 67:467–473CrossRefGoogle Scholar
  12. Cocozza C, Pulvento C, Lavini A, Riccardi M, d’Andria R, Tognetti R (2013) Effects of increasing salinity stress and decreasing water availability on ecophysiological traits of quinoa grown in a Mediterranean-type agroecosystem. J Agron Crop Sci 199:229–240CrossRefGoogle Scholar
  13. Cubas C, Lobo MG, Gonzales M (2008) Optimization of the extraction of chlorophyll in green beans (Phaseolus vulgaris L.) by N,N-dimethylformamide using response surface methodology. J Food Compos Anal 21:125–133CrossRefGoogle Scholar
  14. Curran PJ, Dungan JL, Gholz HL (1990) Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiol 7:33–48PubMedCrossRefGoogle Scholar
  15. Du C-J, Sun D-W (2004) Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci Technol 15:230–249CrossRefGoogle Scholar
  16. Dwyer LM, Tollenaar M, Houwing L (1991) A nondestructive method to monitor leaf greenness in corn. Can J Plant Sci 71:505–509CrossRefGoogle Scholar
  17. Efeoglo B, Ekmekci Y, Cicek N (2009) Physiological responses of three maize cultivars to drought stress and recovery. S Afr J Bot 75:34–42CrossRefGoogle Scholar
  18. Eisenberg D, Crothers D (1979) Physical chemistry with applications to the life sciences. Benjamin/Cummings, Menlo Park, CAGoogle Scholar
  19. Esposti MDD, de Sequeira DL, Pereira PRG, Venegas VHA, Salomao LCC, Filho JAM (2003) Assessment of nitrogenized nutrition of citrus rootstocks using chlorophyll concentrations in the leaf. J Plant Nutr 26:1287–1299CrossRefGoogle Scholar
  20. Fanizza G, Dellagatta C, Bagnulo C (1991) A nondestructive determination of leaf chlorophyll in vitis vinifera. Ann Appl Biol 119:203–205CrossRefGoogle Scholar
  21. Filella I, Serrano I, Serra J, Peñuelas J (1995) Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Sci 35:1400–1405CrossRefGoogle Scholar
  22. Forsyth D, Ponce J (2003) Computer vision: a modern approach. Prentice Hall, New JerseyGoogle Scholar
  23. Fracheboud Y, Jompuk C, Ribaut JM, Stamp P, Leipner J (2004) Genetic analysis of cold-tolerance of photosynthesis in maize. Plant Mol Biol 56:241–253PubMedCrossRefGoogle Scholar
  24. Fukshansky L (1981) Optical properties of plants. In: Smith H (ed) Plants and daylight spectrum. Academic Press, New York, pp 20–30Google Scholar
  25. Govaerts YM, Verstraete MM, Pinty B, Gobron N (1999) Designing optimal spectral indices: a feasibility and proof of concept study. Int J Remote Sens 20:1853–1873CrossRefGoogle Scholar
  26. Gratani L (1992) A nondestructive method to determine chlorophyll content of leaves. Photosynthetica 26:469–473Google Scholar
  27. Guendouz A, Maamari K (2011) Evaluating durum wheat performance and efficiency of senescence parameter usage in screening under Mediterranean conditions. Electron J Plant Breed 2(3):400–404Google Scholar
  28. Hafsi M, Mechenneche W, Bouamama L, Djekoune A, Zaharieva M, Monneveux P (2000) Flag leaf senescence and evaluated by numerical image analysis and its relationship with yield under drought in durum wheat. J Agron Crop Sci 185:275–280CrossRefGoogle Scholar
  29. Hu H, Liu HQ, Zhang H, Zhu JH, Yao XG, Zhang XB, Zheng KF (2010) Assessment of chlorophyll content based on image colour analysis, comparison with SPAd-502. In: Proceedings of 2nd International Conference on Information Engineering and Computer Science (ICIECS), Wuhan, ChinaGoogle Scholar
  30. Hunt RWG (2004) The reproduction of colour (6th edn.). In: Imaging science and technology. Wiley-IS&T Series, Chichester, pp 11–12Google Scholar
  31. Jacobsen S-E, Mujica A, Ortiz R (2003) The global potential for quinoa and other Andean crops. Food Rev Int 19:139–148CrossRefGoogle Scholar
  32. Jacobsen S-E, Jensen CR, Pedersen H (2005) Use of the relative vegetation index for growth estimation in quinoa (Chenopodium quinoa Willd.). J Food Agric Environ 3:241–247Google Scholar
  33. Jacobsen S-E, Liu F, Jensen CR (2009) Does root-sourced ABA play a role for regulation of stomata under drought in quinoa (Chenopodium quinoa Willd.). Sci Hortic 122:281–287CrossRefGoogle Scholar
  34. Jeffrey SW, Humphrey GF (1975) New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. Biochem Physiol Pflanzen 167:191–194Google Scholar
  35. Kawashima S, Nakatani M (1998) An algorithm for estimating chlorophyll content in leaves using a video camera. Ann Bot 81:49–54CrossRefGoogle Scholar
  36. Khaleghi E, Arzani K, Moallemi N, Barzegar M (2012) Evaluation of chlorophyll content and chlorophyll fluorescence parameters and relationships between chlorophyll a, b and chlorophyll content index under water stress in Olea europaea cv. Dezful. World Acad Sci Eng Technol 68:1154–1157Google Scholar
  37. Khosh KA, Ando B (1995) Effect of food environments, particularly sodium ion on the synthesis of chlorophyll and plant growth C4. Abstracts Third Crop Science Congress of Iran, Tabriz University, p 14Google Scholar
  38. Liu Y, Zhang T, Whang J (2008) Photosynthesis and metabolite levels in dehydrating leaves of Reaumuria soongorica. Acta Biol Cracov Bot 50:19–26Google Scholar
  39. Markwell J, Osterman JC, Mitchell JL (1995) Calibration of the Minolta SPAD-502 leaf chlorophyll meter. Photosynth Res 46:467–472PubMedCrossRefGoogle Scholar
  40. Minolta (1989) Chlorophyll meter SPAD-502, of chlorophyll content based on image colour Instruction manual. Minolta Co., Ltd., Radiometric analysis, comparison with SPAd-502, paper Instruments Operations, Osaka, JapanGoogle Scholar
  41. Monje O, Bugbee B (1992) Inherent limitations of nondestructive chlorophyll meters: a comparison of two types of meters. HortScience 27:69–71PubMedGoogle Scholar
  42. Netto AT, Campostrini E, Oliveira JG, Yamanishi OK (2002) Portable chlorophyll meter for the quantification of photosynthetic pigments, nitrogen and the possible use for assessment of the photochemical process in Carica papaya L. Braz J Plant Physiol 14:203–210CrossRefGoogle Scholar
  43. Netto AT, Campostrini E, Oliveria GJ, Bressan-Smith RE (2005) Photosynthetic pigments nitrogen, chlorophyll a fluorescence and SPAD-502 readings in coffee leaves. Sci Hortic 104:199–209CrossRefGoogle Scholar
  44. Noboru O, Robertson AR (2005) 3.9: standard and supplementary illuminants. Colorimetry. Wiley, Chichester, pp 92–96. doi:10.1002/0470094745.ch3..ISBN 0-470-09472-9
  45. Pastenes C, Horton P (1996) Effect of high temperature on photosynthesis in beans. II. CO, assimilation and metabolite contents. Plant Physiol 112:1253–1260PubMedCentralPubMedGoogle Scholar
  46. Pérez A, López F, Benlloch J, Christensen S (2000) Colour and shape analysis techniques for weed detection in cereal fields. Comput Electron Agric 25:197–212CrossRefGoogle Scholar
  47. Porra RJ, Thompson WA, Kriedmann PE (1989) Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophylls a and b extracted with four different solvents: verification of the concentration of chlorophyll standards by atomic absorption spectroscopy. Biochim Biophys Acta 975:384–394CrossRefGoogle Scholar
  48. Pulvento C, Riccardi M, Lavini A, d’Andria R, Iafelice G, Marconi E (2010) Field trial evaluation of two Chenopodium quinoa’s genotypes grown in rainfed conditions in a mediterranean environment of south Italy. J Agron Crop Sci 196(6):407–411CrossRefGoogle Scholar
  49. Pulvento C, Riccardi M, Lavini A, Iafelice G, Marconi E, d’Andria R (2012) Yield and quality characteristics of quinoa grown in open field under different saline and non-saline irrigation regimes. J Agron Crop Sci 198(4):254–263CrossRefGoogle Scholar
  50. Richardson AD, Duigan SP, Berlyn GP (2002) An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytol 153:185–194CrossRefGoogle Scholar
  51. Schanda J (2007) 3. CIE Colorimetry. Colorimetry: understanding the CIE system. Wiley, Chichester, pp 43, 44. doi:10.1002/9780470175637.ch3. ISBN 978-0-470-04904-4
  52. Schrevens E, Raeymaeckers L (1992) Colour characterization of golden delicious apples using digital image. Acta Hortic 304:159–161Google Scholar
  53. Steele MR, Gitelson AA, Rundquist DC (2008) A comparison of two techniques for nondestructive measurement of chlorophyll content in grapevine leaves. Agron J 10(3):779–782CrossRefGoogle Scholar
  54. Su CH, Fu CC, Chang YC, Nair GR, Ye JL, Chu LM, Wu WT (2008) Simultaneous estimation of chlorophyll a and lipid contents in microalgae by three color analysis. Biotechnol Bioeng 99:1034–1039PubMedCrossRefGoogle Scholar
  55. Sudhir P, Murthy SDS (2004) Effects of salt stress on basic processes of photosynthesis. Photosynthetica 42(2):481–486CrossRefGoogle Scholar
  56. Ting ASY, Tan LM, Ling APK (2009) In vitro assessment of tolerance of Orthosiphon stamineus to induced water and salinity stress. Asian J Plant Sci 8:206–211CrossRefGoogle Scholar
  57. Turner FT, Jund MF (1991) Chlorophyll meter to predict N topdress requirement of Semidwarf rice. Agron J 83:926–928CrossRefGoogle Scholar
  58. Uddling J, Gelang-Alfredsson J, Piikki K, Pleijel H (2007) Evaluating the relationship between SPAD-502 chlorophyll meter readings and leaf chlorophyll concentration. Photosynth Res 91:37–46PubMedCrossRefGoogle Scholar
  59. Vervaeke F, Schrevens E, Verreydt J, Portier K, De Baerdemaeker J (1993) The use of digitized video images for monitoring color and color evolution of Jonagold apples during shelf life. In: Proceedings of the sixth international congress on engineering and food. Chiba, Japan. London, UK: Blackie Academic and Professional, pp 200–202Google Scholar
  60. Viña A, Gitelson AA (2005) New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops. Geophys Res Lett 32:L17403CrossRefGoogle Scholar
  61. Xu W, Rosenow DT, Nguyen HT (2000) Stay green trait in grain sorghum: relationship between visual rating and leaf chlorophyll concentration. Plant Breed 119:365–367CrossRefGoogle 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 Tiss Organ Cult 100:183–188CrossRefGoogle Scholar
  63. Yamamoto A, Nakamura T, Adu-Gyamfi JJ, Saigusa M (2002) Relationship between chlorophyll content in leaves of sorghum and pigeon pea determined by extraction method and by chlorophyll meter (SPAD-502). J Plant Nutr 25:2295–2301CrossRefGoogle Scholar
  64. Zhang M, De Baerdemaeker J, Schrevens E (2003) Effects of different varieties and shelf storage conditions of chicory on deteriorative color changes using digital image processing and analysis. Food Res Int 36(7):669–676CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • M. Riccardi
    • 1
  • G. Mele
    • 1
  • C. Pulvento
    • 1
  • A. Lavini
    • 1
  • R. d’Andria
    • 1
  • S. -E. Jacobsen
    • 2
  1. 1.CNR - Institute for Agricultural and Forest Mediterranean Systems (ISAFoM)ErcolanoItaly
  2. 2.Department of Plant and Environmental Sciences, Faculty of ScienceUniversity of CopenhagenTaastrupDenmark

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