, Volume 48, Issue 1, pp 9–15 | Cite as

Modeling individual leaf area of rose (Rosa hybrida L.) based on leaf length and width measurement

  • Y. Rouphael
  • A. H. Mouneimne
  • A. Ismail
  • E. Mendoza-De Gyves
  • C. M. Rivera
  • G. Colla
Original Papers


Accurate and nondestructive methods to determine individual leaf areas of plants are a useful tool in physiological and agronomic research. Determining the individual leaf area (LA) of rose (Rosa hybrida L.) involves measurements of leaf parameters such as length (L) and width (W), or some combinations of these parameters. Two-year investigation was carried out during 2007 (on thirteen cultivars) and 2008 (on one cultivar) under greenhouse conditions, respectively, to test whether a model could be developed to estimate LA of rose across cultivars. Regression analysis of LA vs. L and W revealed several models that could be used for estimating the area of individual rose leaves. A linear model having L×W as the independent variable provided the most accurate estimate (highest r 2 , smallest MSE, and the smallest PRESS) of LA in rose. Validation of the model having L×W of leaves measured in the 2008 experiment coming from other cultivars of rose showed that the correlation between calculated and measured rose LA was very high. Therefore, this model can estimate accurately and in large quantities the LA of rose plants in many experimental comparisons without the use of any expensive instruments.

Additional key words

individual leaf area linear measurements nondestructive methods Rosa hybrida L. validation 



general linear model


leaf midvein length


individual leaf area

L × W

product leaf length and width


leaf shape


mean square error


mean squared prediction error


observed leaf area


predicted leaf area


prediction sum of squares


error sum of squares


tolerance values


variance inflation factor


maximum leaf width


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Antunes, W.C., Pompelli, M.F., Carretero, D.M., DaMatta, F.M.: Allometric models for non-destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora. — Ann. Appl. Biol. 153: 33–40, 2008.CrossRefGoogle Scholar
  2. Beerling, D.J., Fry, J.C.: A comparison of the accuracy, variability and speed of five different methods for estimating leaf area. — Ann. Bot. 65: 483–488, 1990.Google Scholar
  3. Bignami, C., Rossini, F.: Image analysis estimation of leaf area index and plant size of young hazelnut plants. — J. Hort. Sci. 71: 113–121, 1996.Google Scholar
  4. Bland, J.M., Altman, D.G.: Statistical methods for assessing agreement between two methods of clinical measurements — Lancet 1: 307–310, 1986.PubMedGoogle Scholar
  5. Cankaya, S., Kayaalp, G.Y., Sangun, L., Tahtali, Y., Akar, M.: A comparative study of estimation methods for parameters in multiple linear regression model. — J. Appl. Animal Res. 29: 43–47, 2006.Google Scholar
  6. Cho, Y.Y., Oh, S., Oh, M.M., Son, J.E.: Estimation of individual leaf area, fresh weight, and dry weight of hydroponically grown cucumbers (Cucumis sativus L.) using leaf length, width, and SPAD value. — Sci. Hort. 111: 330–334, 2007.CrossRefGoogle Scholar
  7. Cristofori, V., Fallovo, C., Mendoza-de Gyves, E., Rivera, C.M., Bignami, C., Rouphael, Y.: Non-destructive, analogue model for leaf area estimation in persimmon (Diospyros kaki L.f.) based on leaf length and width measurement. — Eur. J. Hort. Sci. 73: 216–221, 2008.Google Scholar
  8. Cristofori, V., Rouphael, Y., Mendoza-de Gyves, E., Bigniami, C.: A simple model for estimating leaf area of hazelnut from linear measurements. — Sci. Hort. 113: 221–225, 2007.CrossRefGoogle Scholar
  9. Daughtry, C.: Direct measurements of canopy structure. — Remore Sens. Rev. 5: 45–60, 1990.Google Scholar
  10. Demirsoy, H., Demirsoy, L., Uzun, S., Ersoy, B.: Nondestructive leaf area estimation in peach. — Eur. J. Hort. Sci. 69: 144–146, 2004.Google Scholar
  11. De Swart, E.A.M., Groenwold, R., Kanne, H.J., Stam, P., Marcelis, L.F.M., Voorrips, R.E.: Non-destructive estimation of leaf area for different plant ages and accessions of Capsicum annuum L. — J. Hort. Sci. Biotechnol. 79: 764–770, 2004.Google Scholar
  12. Fallovo, C., Cristofori, V., Mendoza-de Gyves, E., Rivera, C.M., Fanasca, S., Bignami, C., Sassine, Y., Rouphael, Y.: Leaf area estimation model for small fruits from linear measurements. — HortScience 43: 2263–2267, 2008.Google Scholar
  13. Fascella, G., Maggiore, P., Zizzo, G., Colla, G., Rouphael, Y.: A simple and low-cost method for leaf area measurement in Euphorbia × lomi Thai hybrids. — Adv. Hort. Sci. 23: 57–60, 2009.Google Scholar
  14. Gill, J.L.: Outliers, residuals, and influence in multiple regression. — J. Anim. Breed. Genet. 103:161–175, 1986.CrossRefGoogle Scholar
  15. Kandiannan, K., Parthasarathy, U., Krishnamurthy, K.S., Thankamani, C.K., Srinivasan, V.: Modeling individual leaf area of ginger (Zingiber officinale Roscoe) using leaf length and width. — Sci. Hort. 120: 532–537, 2009.CrossRefGoogle Scholar
  16. Kumar, R.: Calibration and validation of regression model for non-destructive leaf area estimation of saffron (Crocus sativus L.). — Sci Hort. 122: 142–145, 2009.CrossRefGoogle Scholar
  17. Lizaso, J.I., Batchelor, W.D., Westgate, M.E.: A leaf area model to simulate cultivar-specific expansion and senescence of maize leaves. — Field Crops Res. 80: 1–17, 2003.CrossRefGoogle Scholar
  18. Marini, R.P.: Estimating mean fruit weight and mean fruit value for apple trees: comparison of two sampling methods with the true mean. — J. Amer. Soc. Hort. Sci. 126: 503–510, 2001.Google Scholar
  19. Marquardt, D.W.: Generalized inverse, ridge regression, biased linear estimation, and nonlinear estimation. — Technometrics 12: 591–612, 1970.CrossRefGoogle Scholar
  20. Mendoza-de Gyves, E., Rouphael, Y., Cristofori, V., Rosana Mira, F.: A non-destructive, simple and accurate model for estimating the individual leaf area of kiwi (Actinidia deliciosa). — Fruits 62: 171–176, 2007.CrossRefGoogle Scholar
  21. Miranda, C., Royo, J.B.: A statistical model to estimate potential yields in peach before bloom. — J Amer. Soc. Hort. Sci. 128: 297–301, 2003a.Google Scholar
  22. Miranda, C., Royo, J.B.: Statistical model estimates potential yields in pear cultivars ‘Blanquilla’ and ‘Conference’ before bloom. — J. Amer. Soc. Hort. Sci. 128: 452–457, 2003b.Google Scholar
  23. Miranda, C., Royo, J.B.: Statistical model estimates potential yield in “Golden Delicious” and “Royal Gala” apples before bloom. — J. Amer. Soc. Hort. Sci. 129: 20–25, 2004.Google Scholar
  24. Montero, F.J., de Juan, J.A., Cuesta, A., Brasa, A.: Nondestructive method to estimate leaf area in Vitis vinifera L. — HortScience 35: 696–698, 2000.Google Scholar
  25. Neter, J., Kutner, M.H., Nachtshein, C.J., Wasserman, W.: Applied Linear Regression — Models., 3rd Ed. Homewood III, Irwin 1996.Google Scholar
  26. Nyakwende, E., Paull, C.J., Atherton, J.G.: Non-destructive determination of leaf area in tomato plants using image processing. — J. Hort. Sci. 72: 225–262, 1997.Google Scholar
  27. Olfati, J.A., Peyvast, Gh., Sanavi, M., Salehi, M., Mahdipour, M., Nosratie-Rad, Z.: Comparisons of leaf area estimation from linear measurements of red cabbage. — Int. J. Veg. Sci. 15: 185–192, 2009.CrossRefGoogle Scholar
  28. Peksen, E.: Non-destructive leaf area estimation model for faba bean (Vicia faba L.). — Sci. Hort. 113: 322–328, 2007.CrossRefGoogle Scholar
  29. Rivera, C.M., Rouphael, Y., Cardarelli, M., Colla, G.: A simple and accurate equation for estimating individual leaf area of eggplant from linear measurements. — Europ. J. Hort. Sci. 72: 228–230, 2007.Google Scholar
  30. Rouphael, Y., Rivera, C.M., Cardarelli, M., Fanasca, S., Colla, G.: Leaf area estimation from linear measurements in zucchini plants of different ages. — J. Hort. Sci. Biotechnol. 81: 238–241, 2006.Google Scholar
  31. Rouphael, Y., Colla, G., Fanasca, S., Karam, F.: Leaf area estimation of sunflower leaves from simple linear measurements. — Photosynthetica 45: 306–308, 2007.CrossRefGoogle Scholar
  32. Rouphael, Y., Mouneimne, A.H., Rivera, C.M., Cardarelli, M., Marucci, A., Colla, G.: Allometric models for non-destructive leaf area estimation in grafted and ungrafted watermelon (Citrullus lanatusThunb.). — J. Food Sci. Environ. 8: 161–165, 2010.Google Scholar
  33. Salerno, A., Rivera, C.M., Rouphael, Y., Colla, G., Cardarelli, M., Pierandrei, F., Rea, E., Saccardo, F.: Leaf area estimation of radish from linear measurements. — Adv. Hort. Sci. 19: 213–215, 2005.Google Scholar
  34. Serdar, U., Demirsoy, H.: Non-destructive leaf area estimation in chestnut. — Sci. Hort. 108: 227–230, 2006.CrossRefGoogle Scholar
  35. Stoppani, M.I., Wolf, R., Francescangeli, N., Martí, H.R.: A non-destructive and rapid method for estimating leaf area of broccoli. — Adv. Hort. Sci. 17: 173–175, 2003.Google Scholar
  36. Tsialtas, J.T., Maslaris, N.: Leaf area estimation in a sugar beet cultivar by linear models. — Photosynthetica 43: 477–479, 2005.CrossRefGoogle Scholar
  37. Tsialtas, J.T., Koundouras, S., Zioziou, E.: Leaf area estimation by simple measurements and evaluation of leaf area prediction models in Cabernet-Sauvignon grapevine leaves. — Photosynthetica 46: 452–456, 2008.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Y. Rouphael
    • 1
  • A. H. Mouneimne
    • 2
  • A. Ismail
    • 3
  • E. Mendoza-De Gyves
    • 4
  • C. M. Rivera
    • 4
  • G. Colla
    • 4
  1. 1.Department of Crop Production, Faculty of Agricultural Engineering and Veterinary MedicineLebanese UniversityDekwaneh-Al MatenLebanon
  2. 2.Department of Environment and Natural Resources, Faculty of Agricultural Engineering and Veterinary MedicineLebanese UniversityDekwaneh-Al MatenLebanon
  3. 3.Department of Food Technology, Faculty of Agricultural Engineering and Veterinary MedicineLebanese UniversityDekwaneh-Al MatenLebanon
  4. 4.Dipartimento di Geologia e Ingegneria Meccanica, Naturalistica e Idraulica per il TerritorioUniversità della TusciaViterboItaly

Personalised recommendations