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ASPHC Baner

Vol. 20 No. 6 (2021)

Articles

Possibilities of using RGB-based image analysis to estimate the chlorophyll content of micropropagated strawberry plants

DOI: https://doi.org/10.24326/asphc.2021.6.11
Submitted: May 14, 2019
Published: 2021-12-09

Abstract

An image analysis method based on RGB features for rapid estimation of the chlorophyll content of leaves of micropropagated strawberry plants (2 cultivars) was presented in the study. An algorithm describing the relationship between the absolute values of chlorophyll content and the colour components of a leaf image captured with a conventional scanner was developed and tested. The accuracy of the proposed method was compared with that of an optical greenness meters designed for assessing leaf chlorophyll content. The chlorophyll content in the strawberry leaves was correlated with the results of measurements recorded by the two optical meters (SPAD-502, CCM-200) and with RGB values of scans of these leaves. The highest values of correlation coefficients were obtained between the chemical analysis results and mean values of the red colour (R) of the scans. However, varietal differences were evident here, which indicates the need for individual calibrations. In the case of the green colour (G), the accuracy was slightly lower; however, no varietal differences were found, thus one calibration can be used for both cultivars. Three formulas: (R-G)/(R+G), (R-G)/(R+G+B), and R/(R+G+B) were selected and their relationship with the changes in chlorophyll content was tested. These variables did not explain the changes in chlorophyll content better than the variable R. The study confirmed the possibility of using the image capture method for the detection of chlorophyll status in strawberry plantlets cultured in vitro.

References

  1. Ali, M.M., Al-Ani, A., Eamus, D., Tan, D.K.Y. (2012). A new image processing based technique to determine chlorophyll in plants. Am. Euras. J. Agric. Environ. Sc., 12, 1323–1328. https://doi.org/10.5829/idosi.aejaes.2012.12.10.1917
  2. Arigita, L., Gonzalez, A., Sanchez Tames, R. (2002). Influence of CO2 and sucrose on photosynthesis and transpiration of Actidinia deliciosa explants cultured in vitro. Physiol. Plant., 115, 166–173.
  3. Aynalem, H.M., Righetti, T.L., Reed, B.M. (2006). Non-destructive evaluation of in vitro-stored plants: a comparison of visual and image analysis. In Vitro Cell. Dev. Biol. Plant., 42, 562–567. https://doi.org/10.1079/IVP2006816
  4. Borkowska, B. (2001). Morphological and physiological characteristics of micropropagated strawberry plants rooted in vitro and ex vitro. Sci. Hortic., 89, 195–206. https://doi.org/10.1016/S0304-4238(00)00230-2
  5. Boxus, P. (1974). The production of strawberry plants by in vitro micropropagation. J. Hort. Sci., 49, 209–210.
  6. Bruinsma, J. (1963). The quantitative analysis of chlorophyll a and b in plant extracts. Phytochem. Phytobiol., 2, 241–249.
  7. Caliskan, O., Kurt, D., Camas, N., Odabas, M.S. (2020). Estimating chlorophyll concentration index in sugar beet leaves using an artificial neural network. Pol. J. Environ. Stud., 29(1), 25–31. https://doi.org/10.15244/pjoes/95031
  8. Cerovic, Z.G., Masdoumier, G., Ghozlen, N.B., Latouche, G. (2012). A new optical leaf-clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids. Physiol. Plant., 146, 251–260. https://doi.org/10.1111/j.1399-3054.2012.01639.x
  9. Dutta Gupta, S., Ibaraki, Y., Pattanayak, A.K. (2013). Development of a digital image analysis method for real time estimation of chlorophyll content in micropropagated potato plants. Plant Biotechnol. Rep., 7, 91–97. https://doi.org/10.1007/s11816-012-0240-5
  10. Fanizza, G., Della Gatta, C., Bagnulo, C. (1991). A non‐destructive determination of leaf chlorophyll in Vitis vinifera. Ann. Appl. Biol., 119, 203–205. https://doi.org/10.1111/j.1744-7348.1991.tb04858.x
  11. Hawkins, T.S. (2009). Modeling the relationship between extractable chlorophyll and SPAD-502 readings for endangered plant species research. J. Nat. Conserv., 17, 123–127. https://doi.org/10.1016/j.jnc.2008.12.007
  12. Honda, H., Takikawa, N., Noguchi, H., Hanai, T., Kobayashi, T. (1997). Image analysis associated with a fuzzy neural network and estimation of shoot length of regenerated rice callus. J. Ferment. Bioeng., 84, 342–347. https://doi.org/10.1016/S0922-338X(97)89256-2
  13. Ibaraki, Y., Matsumura, K. (2005). Non-destructive evaluation of the photosynthetic capacity of PSII in micropropagated plants. J. Agric. Meteorol., 60, 1073–1076. https://doi.org/10.2480/agrmet.1073
  14. Jiang, Ch., Johkan, M., Hohjo, M., Tsukagoshi, S., Maruo, T. (2017). A correlation analysis on chlorophyll content and SPAD value in tomato leaves. HortResearch, 71, 37–42. https://doi.org/10.20776/S18808824-71-P37
  15. Jifon, J.L., Syvertsen J.P., Whaley E. (2005). Growth environment and leaf anatomy affect nondestructive estimates of chlorophyll and nitrogen in Citrus sp. leaves. J. Amer. Soc. Hort. Sci., 130, 152–158. https://doi.org/10.21273/JASHS.130.2.152
  16. Kahramanoğlu, İ., Ülker, E.D., Ülker, S. (2021). Integrating support vector machine (SVM) technique and contact imaging for fast estimation of the leaf chlorophyll contents of strawberry plants. IJETT, 69(3), 23–28. https://doi.org/10.14445/22315381/IJETT-V69I3P205
  17. Knop, W. (1865). Quantitative Untersuchungen über die Ernahrungsprozesse der Pflanzen. Landwirtsch. Vers. Stn., 7, 93–107.
  18. Magyar-Tábori, K., Dobránszki, J., Hudák, I. (2011). Effect of cytokinin content of the regeneration media on in vitro rooting ability of adventitious apple shoots. Sci. Hort., 129, 910–913. https://doi.org/10.1016/j.scienta.2011.05.011
  19. Markwell, J., Osterman, J. C., Mitchell, J.L. (1995). Calibration of the Minolta SPAD-502 leaf chlorophyll meter. Photosynth. Res., 46, 467–472.
  20. Mohan, P.J., Dutta Gupta, S. (2019). Intelligent image analysis for retrieval of leaf chlorophyll content of rice from digital images of smartphone under natural light. Photosynthetica, 10. https://doi.org/10.32615/ps.2019.046
  21. Monje, O.A., Bugbee, B. (1992). Inherent limitations of nondestructive chlorophyll meters – a comparison of 2 types of meters. HortScience, 27, 69–71.
  22. Murashige, T., Skoog, F. (1962). A revised medium for rapid growth and bioassays with tobacco tissue cultures. Physiol. Plant., 15, 473–497.
  23. Naus, J., Prokopová, J., Rebíček, J., Spundova, M. (2010). SPAD chlorophyll meter reading can be pronouncedly affected by chloroplast movement. Photosynth. Res., 105, 265–271. https://doi.org/10.1007/s11120-010-9587-z
  24. Odabas, M.S., Simsek, H., Lee, C., İşeri, İ. (2017a). Multilayer perceptron neural network approach to estimate chlorophyll concentration index of lettuce (Lactuca sativa L.). Commun. Soil Sci. Plant Anal., 48, 162–169. https://doi.org/10.1080/00103624.2016.1253726
  25. Odabas, M., Senyer, N., Kayhan, G., Ergun, E. (2017b). Estimation of chlorophyll concentration index at leaves using artificial neural networks. J. Circ. Syst. Comput., 26(2), 1750026. https://doi.org/10.1142/S0218126617500268
  26. Özreçberoğlu, N., Kahramanoğlu, İ. 2020. Mathematical models for the estimation of leaf chlorophyll content based on RGB colours of contact imaging with smartphones: a pomegranate example. Folia Hort., 32(1), 57–67. https://doi.org/10.2478/fhort-2020-0006
  27. Pagola, M., Ortiz, R., Irigoyen, I., Bustince, H., Barrenechea, E., Aparicio-Tejo, P., Lamsfus, C., Lasa, B. (2009). New method to assess barley nitrogen nutrition status based on image colour analysis, comparison with SPAD-502. Comput. Electron. Agric., 65, 213–218. https://doi.org/10.1016/j.compag.2008.10.003
  28. Pospísilová, J., Synková, H., Haisel, D., Semorádová, S. (2007). Acclimation of plantlets to ex vitro conditions: effects of air humidity, irradiance, CO2 concentration and abscisic acid (a review). Acta Hort., 748, 29–38. https://doi.org/10.17660/ActaHortic.2007.748.2
  29. Richardson, A.D., Duigan, S.P., Berlyn, G.P. (2002). An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytol., 153, 185–194. https://doi.org/10.1046/j.0028-646X.2001.00289.x
  30. Rorie, R.L., Purcell, L.C., Karcher, D.E., King, A.C. (2011). The assessment of leaf nitrogen in corn from digital images. Crop Sci., 51, 2174–2180. https://doi.org/10.2135/cropsci2010.12.0699
  31. Rybczyński, J.J., Mikuła, A. 2006. Engagement of biotechnol¬ogy in the protection of threatened plant species in Poland. Biodivers. Res. Conserv., 3–4, 361–368.
  32. Spomer, L.A., Smith, M.A.L., Sawwan, J.S. (1988). Rapid nondestructive measurement of chlorophyll content in leaves with nonuniform chlorophyll distribution. Photosynth. Res., 16, 227–284. https://doi.org/10.1007/BF00028845
  33. Tewari, V.K., Arudra, A.K., Kumar, S.P., Pandey, V., Chandel, N.S. (2013). Estimation of plant nitrogen content using digital image processing. Agric. Engineer. Int., CIGR J., 15, 78–86.
  34. Tichá, I., Čáp, F., Pacovská, D., Hofman, P., Haisel, D., Čapková, V., Schäfer, C. (1998). Culture on sugar medium enhances photosynthetic capacity and high light resistance of plantlets grown in vitro. Physiol. Plant., 102, 155–162. https://doi.org/10.1034/j.1399-3054.1998.1020201.x
  35. Treder, W., Klamkowski, K., Kowalczyk, W., Sas, D., Wójcik, K. (2016). Possibilities of using image analysis to estimate the nitrogen nutrition status of apple trees. Zemdirbyste-Agriculture, 103, 319–326. https://doi.org/10.13080/z-a.2016.103.041
  36. Treder, W., Cieśliński, G. (2003). Ocena odżywienia jabłoni azotem za pomocą miernika SPAD-502 [Evaluation of apple nitrogen status using SPAD-502]. Folia Hort., suppl. 2, 168–170.
  37. 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, 37–46. https://doi.org/10.1007/s11120-006-9077-5
  38. Valero-Aracama, C., Kane, M.E., Wilson, S.B., Vu, J.C., Anderson, J., Philman, N.L. (2006). Photosynthetic and carbohydrate status of easy-and difficult-to-acclimatize sea oats (Uniola paniculata L.) genotypes during in vitro culture and ex vitro acclimatization. In Vitro Cell. Dev. Biol. Plant., 42, 572–583. https://doi.org/10.1079/IVP2006822
  39. Van Huylenbroeck, J.M., Piqueras, A., Debergh, P.C. (2000). The evolution of photosynthetic capacity and the antioxidant enzymatic system during acclimatization of micropropagagted Calathea plants. Plant Sci., 155, 59–66.
  40. Vesali, F., Omid, M., Mobli, H., Kaleita, A. (2017). Feasibility of using smart phones to estimate chlorophyll content in corn plants. Photosynthetica, 55, 603–610. https://doi.org/10.1007/s11099-016-0677-9
  41. Wang, Q.B., Chen, M.J., Li, Y.C. (2004). Nondestructive and rapid estimation of leaf chlorophyll and nitrogen status of peace lily using a chlorophyll meter. J. Plant Nutr., 27, 557–569. https://doi.org/10.1081/PLN-120028878
  42. Yadav, S.P., Ibaraki, Y., Dutta Gupta, S. (2010). Estimation of the chlorophyll content of micropropagated potato plants using RGB based image analysis. Plant Cell Tiss. Org. Cult., 100, 183–188. https://doi.org/10.1007/s11240-009-9635-6

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