Relationship Between Leaf Color–Associated Chlorophyll Content and Selected Vegetation Indices in Photinia × fraseri Seedlings

Öz

Leaf color is not only an aesthetic characteristic for plants, but also an important indicator reflecting the physiological status of the plant. This study aimed to determine chlorophyll differences arising depending on leaf color (green and red) in Photinia × fraseri Dress. seedlings and to reveal the relationship between these differences and vegetation indices derived from RGB-based image analyses. The study material consisted of green and red leaves collected from four-year-old seedlings grown under the same environmental conditions. Leaf chlorophyll content was determined using a portable chlorophyll meter (SPAD-502); red (R), green (G), and blue (B) color values of the same leaves were obtained from scanned images using ImageJ software. Based on RGB values, excess green (E×G) index, normalized green–red difference index (NGRDI), visible atmospherically resistant index (VARI), green leaf index (GLI), and greenness index (GI) values were calculated. The obtained data were evaluated using independent samples t-test, Spearman's rank correlation analysis, and regression analyses. The results revealed statistically significant differences between green and red leaves in all RGB color values except the blue value, in SPAD values, and in vegetation indices (p < 0.01). SPAD values in green leaves (79.83) were found to be markedly higher than those in red leaves (30.15). In particular, the E×G index showed higher performance than the other indices in reflecting chlorophyll differences associated with leaf color and in explaining SPAD values. Regression analyses revealed that the relationships between SPAD values and vegetation indices exhibited a non-linear structure and that quadratic models provided higher explanatory power. The findings indicate that RGB-based vegetation indices can be used for the non-destructive and practical monitoring of leaf color–associated physiological differences in ornamental plants such as Photinia × fraseri. 

Anahtar Kelimeler

• Excess green index
• image analysis
• RGB color values
• SPAD-502

Photinia × fraseri Fidanlarında Yaprak Rengine Bağlı Klorofil İçeriği ile Bazı Vejetasyon İndeksleri Arasındaki İlişki

Öz

Yaprak rengi, bitkiler için estetik bir özellik olmanın yanı sıra, bitkinin fizyolojik durumunu da yansıtan önemli bir göstergedir. Bu çalışmada, Photinia × fraseri Dress. fidanlarında yaprak rengine (yeşil ve kırmızı) bağlı olarak ortaya çıkan klorofil farklılıklarının belirlenmesi ve bu farklılıkların RGB tabanlı görüntü analizlerinden türetilen vejetasyon indeksleri ile ilişkisinin ortaya konulması amaçlanmıştır. Çalışma materyalini, aynı çevresel koşullarda yetiştirilen dört yaşındaki fidanlardan alınan yeşil ve kırmızı yapraklar oluşturmuştur. Yaprak klorofil içeriği taşınabilir klorofilmetre (SPAD-502) kullanılarak belirlenmiş; aynı yapraklara ait kırmızı (R), yeşil (G) ve mavi (B) renk değerleri taranmış görüntüler üzerinden ImageJ yazılımı ile elde edilmiştir. RGB verileri kullanılarak aşırı yeşil indeksi (excess green, E×G), normalize edilmiş yeşil–kırmızı fark indeksi (normalized green–red difference index, NGRDI), görünür atmosfere dayanıklı indeks (visible atmospherically resistant index, VARI), yeşil yaprak indeksi (green leaf index, GLI) ve yeşillik indeksi (greenness index, GI) değerleri hesaplanmıştır. Elde edilen veriler bağımsız örneklem t-testi, Spearman sıra korelasyon analizi ve regresyon analizleri ile değerlendirilmiştir. Sonuçlar, mavi renk değeri dışındaki tüm RGB renk değerlerinde, SPAD değerlerinde ve vejetasyon indekslerinde yeşil ve kırmızı yapraklar arasında istatistiksel olarak anlamlı farklılıklar bulunduğunu göstermiştir (p < 0,01). Yeşil yapraklardaki (79,83) SPAD değerleri kırmızı yapraklara (30,15) kıyasla belirgin biçimde daha yüksek bulunmuştur. Özellikle E×G indeksi, yaprak rengine bağlı klorofil farklılıklarını yansıtmada ve SPAD değerlerini açıklamada diğer indekslerden daha yüksek bir performans göstermiştir. Regresyon analizleri, SPAD değerleri ile vejetasyon indeksleri arasındaki ilişkilerin doğrusal olmayan bir yapı sergilediğini ve kuadratik modellerin daha yüksek açıklayıcılık sağladığını ortaya koymuştur. Elde edilen bulgular, RGB tabanlı vejetasyon indekslerinin Photinia × fraseri gibi süs bitkilerinde yaprak rengine bağlı fizyolojik farklılıkların tahribatsız ve pratik bir şekilde izlenmesinde kullanılabileceğini göstermektedir.

Anahtar Kelimeler

• Aşırı yeşil indeksi
• görüntü analizi
• RGB renk değerleri
• SPAD-502

Kaynakça

1. Alvarsson, J. J., Wiens, S. and Nilsson, M. E. (2010). Stress recovery during exposure to nature sound and environmental noise. International Journal of Environmental Research and Public Health, 7(3), 1036–1046. https://doi.org/10.3390/ijerph7031036
2. Atar, F. and Bayraktar, A. (2022). Klorofil içeriğinin tespitinde ekstraksiyon yöntemi ve klorofilmetre (SPAD) ölçümleri arasındaki ilişki. In M. Çolak (Ed.), Ziraat, orman ve su ürünleri alanında uluslararası araştırmalar IV (pp. 5–23). Konya: Eğitim Yayınevi.
3. Atar, F., Bayraktar, A. and Güney, D. (2023). Determination of chlorophyll content variation dependent on direction and species using a portable chlorophyll meter (SPAD). In D. Güloğlu (Ed.), Academic analysis and discussions in agriculture, forestry and fisheries (pp. 145–161). Afyonkarahisar: Yaz Yayınları.
4. Atar, F., Güney, D., Bayraktar, A., Yıldırım, N. and Turna, İ. (2020). Seasonal change of chlorophyll content (SPAD value) in some tree and shrub species. Turkish Journal of Forest Science, 4(2), 245–256.
5. Barman, U. and Choudhury, R. D. (2022). Smartphone image based digital chlorophyll meter to estimate the value of citrus leaves chlorophyll using linear regression, LMBP-ANN and SCGBP-ANN. Journal of King Saud University – Computer and Information Sciences, 34(6), 2938–2950. https://doi.org/10.1016/j.jksuci.2020.01.005
6. Bowler, D. E., Buyung-Ali, L. M., Knight, T. M. and Pullin, A. S. (2010). A systematic review of evidence for the added benefits to health of exposure to natural environments. BMC Public Health, 10(1), 456. https://doi.org/10.1186/1471-2458-10-456
7. Castelli, F., Contillo, R. and Miceli, F. (1996). Non-destructive determination of leaf chlorophyll content in four crop species. Journal of Agronomy and Crop Science, 177, 275–283.
8. Cen, H., Wan, L., Zhu, J., Li, Y., Li, X., Zhu, Y., … and He, Y. (2019). Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras. Plant Methods, 15(1), 32. https://doi.org/10.1186/s13007-019-0418-8

9. Cerovic, Z. G., Masdoumier, G., Ghozlen, N. B. and Latouche, G. (2012). A new optical leaf-clip meter for simultaneous non-destructive assessment of leaf chlorophyll and epidermal flavonoids. Physiologia Plantarum, 146, 251–260. https://doi.org/10.1111/j.1399-3054.2012.01639.x
10. Chang, S. X. and Robison, D. J. (2003). Nondestructive and rapid estimation of hardwood foliar nitrogen status using the SPAD-502 chlorophyll meter. Forest Ecology and Management, 181(3), 331–338. https://doi.org/10.1016/S0378-1127(03)00004-5
11. Crawley, M. J. (2013). The R book (2nd ed.). Chichester, UK: Wiley.
12. Cutolo, E. A., Guardini, Z., Dall'Osto, L. and Bassi, R. (2023). A paler shade of green: Engineering cellular chlorophyll content to enhance photosynthesis in crowded environments. New Phytologist, 239(5), 1567–1583. https://doi.org/10.1111/nph.19064
13. Dong, T., Shang, J., Chen, J. M., Liu, J., Qian, B., Ma, B., … and Zhou, G. (2019). Assessment of portable chlorophyll meters for measuring crop leaf chlorophyll concentration. Remote Sensing, 11(22), 2706. https://doi.org/10.3390/rs11222706
14. Du, S., Liu, X., Zhang, D., Zhang, X., Huang, L., Zhao, X., … and Xu, Y. (2018). Growth monitoring of weak gluten wheat using visible and multispectral UAV imagery. In Proceedings of the 7th International Conference on Agro-geoinformatics (pp. 1–6). IEEE.
15. Edo, N. Y. (2025). Enhancing cutting propagation in Photinia × fraseri plant through the synergistic effect of IBA, glycerol, and growing media mixture. Zanin Journal of Science and Engineering, 1(3), 80–92. https://doi.org/10.64362/zjse.86
16. Elsadek, M., Sun, M., Sugiyama, R. and Fujii, E. (2019). Cross-cultural comparison of physiological and psychological responses to different garden styles. Urban Forestry & Urban Greening, 38, 74–83. https://doi.org/10.1016/j.ufug.2018.11.007
17. Gitelson, A. A., Kaufman, Y. J., Stark, R. and Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing and Environment, 80(1), 76–87. https://doi.org/10.1016/S0034-4257(01)00289-9
18. Gobron, N., Pinty, B., Verstraete, M. M. and Widlowski, J. L. (2000). Advanced vegetation indices optimized for up-coming sensors: Design, performance, and applications. IEEE Transactions on Geoscience and Remote Sensing, 38(6), 2489–2505.
19. Holzman, D., Meletaki, V., Bobrow, I., Weinberger, A., Jivraj, R. F., Green, A. and Chatterjee, A. (2025). Natural beauty and human potential: Examining aesthetic, cognitive, and emotional states in natural, biophilic, and control environments. Journal of Environmental Psychology, 104, 102591. https://doi.org/10.1016/j.jenvp.2025.102591
20. Hunt, E. R., Cavigelli, M., Daughtry, C. S. T., McMurtrey, J. E. and Walthall, C. L. (2005). Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture, 6(4), 359–378.
21. Hunt, E. R., Daughtry, C. S. T., Eitel, J. U. H. and Long, D. S. (2011). Remote sensing leaf chlorophyll content using a visible band index. Agronomy Journal, 103(4), 1090–1099. https://doi.org/10.2134/agronj2010.0395 Hunt, R. W. G. and Pointer, M. R. (2011). Measuring colour (4th ed.). John Wiley & Sons.
22. Jaleel, C. A., Gopi, R. and Panneerselvam, R. (2008). Growth and photosynthetic pigments responses of two varieties of Catharanthus roseus to triadimefon treatment. Comptes Rendus Biologies, 331(4), 272–277. https://doi.org/10.1016/j.crvi.2008.01.004
23. Janeczko, E., Czyżyk, K., Korcz, N., Woźnicka, M. and Bielinis, E. (2023). The psychological effects and benefits of using green spaces in the city: A field experiment with young Polish adults. Forests, 14(3), 497. https://doi.org/10.3390/f14030497
24. Karcher, D. E. and Richardson, M. D. (2003). Quantifying turfgrass color using digital image analysis. Crop Science, 43(3), 943–951.
25. Kawashima, S. and Nakatani, M. (1998). An algorithm for estimating chlorophyll content in leaves using a video camera. Annals of Botany, 81(1), 49–54.
26. Kytridis, V. P. and Manetas, Y. (2006). Mesophyll versus epidermal anthocyanins as potential in vivo antioxidants: Evidence linking the putative antioxidant role to the proximity of oxy-radical source. Journal of Experimental Botany, 57(10), 2203–2210. https://doi.org/10.1093/jxb/erj185
27. Larraburu, E. E., Apóstolo, N. M. and Llorente, B. E. (2010). Anatomy and morphology of photinia (Photinia × fraseri Dress) in vitro plants inoculated with rhizobacteria. Trees, 24(4), 635–642. https://doi.org/10.1007/s00468-010-0433-x
28. Li, X., Li, J., Liu, A., Zhang, Y. and Zhao, K. (2025). Effects of different agricultural wastes on the growth of Photinia × fraseri under natural low-temperature conditions. Horticulturae, 11(9), 1055. https://doi.org/10.3390/horticulturae11091055
29. Li, X., Zhang, W., Niu, D. and Liu, X. (2024). Effects of abiotic stress on chlorophyll metabolism. Plant Science, 342, 112030. https://doi.org/10.1016/j.plantsci.2024.112030
30. Lichtenthaler, H. K. and Wellburn, A. R. (1983). Determination of total carotenoids and chlorophylls a and b of leaf extracts in different solvents. Biochemical Society Transactions, 11(5), 591–592. https://doi.org/10.1042/bst0110591
31. Liu, Y., Hatou, K., Aihara, T., Kurose, S., Akiyama, T., Kohno, Y., … and Omasa, K. (2021). A robust vegetation index based on different UAV RGB images to estimate SPAD values of naked barley leaves. Remote Sensing, 13(4), 686. https://doi.org/10.3390/rs13040686
32. Mao, Z. H., Deng, L., Duan, F. Z., Li, X. J. and Qiao, D. Y. (2020). Angle effects of vegetation indices and the influence on prediction of SPAD values in soybean and maize. International Journal of Applied Earth Observation and Geoinformation, 93, 102198. https://doi.org/10.1016/j.jag.2020.102198
33. Nishio, J. N. (2000). Why are higher plants green? Evolution of the higher plant photosynthetic pigment complement. Plant, Cell & Environment, 23(6), 539–548.
34. Özdamar, K. (1999). Paket programlar ile istatistiksel veri analizi: SPSS MINITAB (4. baskı). Eskişehir, Türkiye: Kaan Kitabevi.
35. Özkan, Y. (2003). Uygulamalı istatistik 2. İstanbul, Türkiye: Sakarya Kitabevi.
36. Quinn, G. P. and Keough, M. J. (2002). Experimental design and data analysis for biologists. Cambridge, UK: Cambridge University Press.
37. Rawat, U. S. and Agarwal, N. K. (2015). Biodiversity: Concept, threats and conservation. Environment Conservation Journal, 16(3), 19–28.
38. Reis, A. R., Favarin, J. L., Malavolta, E., Júnior, J. L. and Moraes, M. F. (2009). Photosynthesis, chlorophylls, and SPAD readings in coffee leaves in relation to nitrogen supply. Communications in Soil Science and Plant Analysis, 40(9–10), 1512–1528. https://doi.org/10.1080/00103620902820373
39. Richardson, A. D., Duigan, S. P. and Berlyn, G. P. (2002). An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytologist, 153(1), 185–194.
40. Saberioon, M. M., Amin, M. S. M., Anuar, A. R., Gholizadeh, A., Wayayok, A. and Khairunniza-Bejo, S. (2014).
41. Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. International Journal of Applied Earth Observation and Geoinformation, 32, 35–45. https://doi.org/10.1016/j.jag.2014.03.018
42. Signorelli, S., Casaretto, E., Etchemendy-Gamundi, M., Bentancor, M. and Harvey Millar, A. (2025). The green index: A widely accessible method to quantify the degree of greenness of photosynthetic organisms. Plant, Cell & Environment, 48(11), 8027–8043. https://doi.org/10.1111/pce.70102
43. Tang, Y., Fang, Z., Liu, M., Zhao, D. and Tao, J. (2020). Color characteristics, pigment accumulation and biosynthetic analyses of leaf color variation in herbaceous peony (Paeonia lactiflora Pall.). 3 Biotech, 10(2), 76. https://doi.org/10.1007/s13205-020-2063-3
44. Taques, R. C. V., Giloni-Lima, P. C. and Lima, V. A. (2021). Vegetation inside: A comparative analysis of leaf pigments and chlorophyll fluorescence in variegated plants. Scientia Plena, 17(9), 1–12. https://doi.org/10.14808/sci.plena.2021.094901
45. Toța, C. and Sala, F. (2023). Photosynthetic pigments distributions in two leaves categories in Photinia fraseri ‘Red Robin’. Journal of Horticulture, Forestry and Biotechnology, 27(2), 98–104.
46. Treder, W., Klamkowski, K., Sowik, I. and Maciorowski, R. (2021). Possibilities of using RGB-based image analysis to estimate the chlorophyll content of micropropagated strawberry plants. Acta Scientiarum Polonorum. Hortorum Cultus, 20(6), 105-115. https://doi.org/10.24326/asphc.2021.6.11
47. Trussell, H. J., Saber, E. and Vrhel, M. (2005). Color image processing (Basics and special issue overview). IEEE Signal Processing Magazine, 22(1), 14–22.
48. Tyrväinen, L., Ojala, A., Korpela, K., Lanki, T., Tsunetsugu, Y. and Kagawa, T. (2014). The influence of urban green environments on stress relief measures: A field experiment. Journal of Environmental Psychology, 38, 1–9. https://doi.org/10.1016/j.jenvp.2013.12.005
49. Vesali, F., Omid, M., Mobli, H. and Kaleita, A. (2017). Feasibility of using smart phones to estimate chlorophyll content in corn plants. Photosynthetica, 55(4), 603–610. https://doi.org/10.1007/s11099-016-0677-9
50. Wang, R., Helbich, M., Yao, Y., Zhang, J., Liu, P., Yuan, Y. and Liu, Y. (2019). Urban greenery and mental wellbeing in adults: Cross-sectional mediation analyses on multiple pathways across different greenery measures. Environmental Research, 176, 108535. https://doi.org/10.1016/j.envres.2019.108535
51. Wang, Z., Tan, X., Ma, Y., Liu, T., He, L., Yang, F., … and Ma, J. (2024). Combining canopy spectral reflectance and RGB images to estimate leaf chlorophyll content and grain yield in rice. Computers and Electronics in Agriculture, 221, 108975. https://doi.org/10.1016/j.compag.2024.108975
52. Woebbecke, D. M., Meyer, G. E., Von Bargen, K. and Mortensen, D. A. (1995). Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE, 38, 259–269. https://doi.org/10.13031/2013.27838
53. Yadav, S. P., Ibaraki, Y. and Dutta Gupta, S. (2010). Estimation of the chlorophyll content of micropropagated potato plants using RGB based image analysis. Plant Cell, Tissue and Organ Culture, 100(2), 183–188. https://doi.org/10.1007/s11240-009-9635-6
54. Yang, Y., Chen, X., Xu, B., Li, Y., Ma, Y. and Wang, G. (2015). Phenotype and transcriptome analysis reveals chloroplast development and pigment biosynthesis together influenced the leaf color formation in mutants of Anthurium andraeanum ‘Sonate’. Frontiers in Plant Science, 6, 139. https://doi.org/10.3389/fpls.2015.00139
55. Yuan, Y., Wang, X., Shi, M. and Wang, P. (2022). Performance comparison of RGB and multispectral vegetation indices based on machine learning for estimating Hopea hainanensis SPAD values under different shade conditions. Frontiers in Plant Science, 13, 928953. https://doi.org/10.3389/fpls.2022.928953
56. Zhang, Q., Zhang, M., Ding, Y., Zhou, P. and Fang, Y. (2018). Composition of photosynthetic pigments and photosynthetic characteristics in green and yellow sectors of the variegated Aucuba japonica ‘Variegata’ leaves. Flora, 240, 25–33. https://doi.org/10.1016/j.flora.2017.12.010
57. Zhang, R., Yang, P., Liu, S., Wang, C. and Liu, J. (2022). Evaluation of the methods for estimating leaf chlorophyll content with SPAD chlorophyll meters. Remote Sensing, 14(20), 5144. https://doi.org/10.3390/rs14205144