Leaflet shape analysis separates rose cultivars and estimates leaf area

Yıl 2019, Cilt: 34 Sayı: 3, 220 - 226, 15.10.2019

Mansour Matloobi , Sepideh Tahmasebi Faribourz Zare Nahandi Alireza Motallebi-azar

https://doi.org/10.7161/omuanajas.484655

Öz

Trying to use tip leaflet of rose plants as a
sample to estimate leaf area and to separate rose cultivars, in an experiment
we took leaf images from three different stem layers of four garden roses.
After preliminary image pre-processing measures, some important leaf geometric
features such as leaf and leaflet area, perimeter, circularity and leaflet
length and width were measured or calculated. Analysis of variance showed that
it would be possible to separate rose cultivars by including only two leaf
properties, i.e., tip leaflet angle and leaflet area to leaf area ratio. It was
also determined that three leaf layers along the rose stem can be recognized
and categorized by implementing just angle of tip leaflet. Leaflet area was
agreeably approximated by fitting a simple linear model to the product of
leaflet minor and major axes. Further analyses indicated that some leaflet
properties such as solidity, perimeter and circularity can be used as
significant criteria to distinguish rose cultivars, however other features like
leaflet elongation and rectangularity were quite poor and insignificant in this
case. In conclusion, it was determined that rose leaflet tip angle not only has
the ability of being as a good morphometric marker in separating rose stem leaf
layers but also it is capable of identifying different rose cultivars. 

Anahtar Kelimeler

image analysis, leaf morphometric features, leaf area, rose, Leaflet shape

Kaynakça

• Abramoff MD, Magalhaes PJ, Ram SJ. 2004. Image processing with imageJ. Biophotonics International. 11: 36-42.
• Ashok AD, Rengasamy P. 2000. Effect of nitrogen fertigation at different levels and sources on the Growth of cut rose cv. First Red under greenhouse conditions. South Indian Hortic. 48:139-141.
• Antunes WC, Pompelli MF, Carretero DM, DaMatta FM. 2008. Allometric models for non-destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora). Annals of Applied Biology. 153:33-40.
• Aravanopoulos FA. 2005. Phenotypic variation and population relationships of chestnut (Castanea sativa) in greece, Revealed by Multivariate Analysis of Leaf Morphometrics. Acta Hortic. 693: 233-240.
• Arunpriya C, Thanamani AS. 2014. An effective tea leaf recognition algorithm for plant classification using radial basis function machine. Journal of Modern Engineering Research. 4:35-44.
• Baret F, Coppin P, Fleck S, Jonckheere I, Muys B, Nackaerts K, Weiss M. 2004. Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography. Agr Forest Meteorol. 121:19-35.
• Bell A, Bryan A. 2008. Plant Form: An illustrated guide to flowering plant morphology. London: Timber Press. 432.
• Blanco FF, Folegatti MV. 2005. Estimation of leaf area for greenhouse cucumber by linear measurements under salinity and grafting. Scientia Agricola. 62: 305-309.
• Boote KJ, Jones JW, Hoogenboom G. 1986. Research and management application of the PNUTGRO crop growth model. Proc. Am. Peanut Res. Edu. Soc. 20: 57.
• Bredmose N, Hansen J, Nielsen J. 2001. Topophysic influences on rose bud and shoot growth and flower development are determined by endogenous axillary bud factors, in III International Symposium on Rose Research and Cultivation, eds N. Zieslin and H.Agbaria (Herzliya: International Society Horticultural Science).177–183.
• Carins murphy MR, Jordan GJ, Brodribb TJ. 2012. Differential leaf expansion can enable hydraulic acclimation to sun and shade. Plant, Cell and Environment. 35: 1407-1418.
• Cerutti GL, Tougne A, Vacavant, Coquin D. 2011. A parametric active polygon for leaf segmentation and shape estimation. In International Symposium on Visual Computing (ISVC). 202–213.
• Chitwood DH, Headland LR, Filiault DL, Kumar R, Jimenez Gomez JM, Schrager AV. 2012. Native environment modulates leaf size and response to simulated foliar shade across wild tomato species. PLoS ONE. 7:e29570.
• Chitwood DH, Headland LR, Kumar R, Peng J, Maloof JN, Sinha NR. 2012 b. The developmental trajectory of leaflet morphology in wild tomato species. Plant Physiol. 158:1230-1240.
• Chitwood DH, Kumar R, Headland LR, Ranjan A, Covington MF, Ichihashi Y, Fulop D, Jimnez-Gomez JM, Peng J, Maloof JN. 2013. A quantitative genetic basis for leaf morphology in a set of precisely defined tomato introgression lines. Plant Cell. 25: 2465-2481.
• Cristofori V, Rouphael Y, Mendoza-de Gyves E, Bignami C. 2007. A simple model for estimating leaf area of hazelnut from linear measurements.ScientiaHorticulturae. 113: 221-225.
• Cookson SJ, Chenu K, Granier C. 2007. length affects the dynamics of leaf expansion and cellular development in Arabidopsis thaliana partially through floral transition timing. Ann. Bot. 99:703-711.
• Cuizhi G, Robertson KR. 2003. Rosa Linnaeus, Sp. Flora of China. 9: 339-381.
• Demotes MS, Huche-Thelier L, Morel P, Boumaza R, Guerin V, Sakr S. 2013.Temporary water restriction or light intensity limitation promotes branching in rosebush. Sci. Hortic. 150: 432–440.
• Du J, Huang D, Wang X, Gu X. 2006. Computer-aided plant species identification (CAPSI) based on leaf shape matching technique. Transactions of the Institute of Measurement and Control. 28: 275-284.
• Dutta GS, Ibaraki Y, Pattanayak AK. 2013. Development of a digital image analysis method for real-time estimation of chlorophyll content in micro propagated potato plants. Plant Biotechnol. 7:91–97.
• Eftekhari M, BKamkar B , Alizadeh M. 2011. Prediction of leaf area in some Iranian table grape (Vitis vinifera L.) cuttings by a non-destructive and simple method. Science Research Reporter .1:115-121.
• Ellis B, Daly DC, Hickey LJ, JohnsonKR, Mitchell JD, Wilf P. 2009. Manual of leaf architecture. Cornell University Press, Ithaca, New York, USA.
• Enoch HZ, RG Hurd. 1979. The effect of elevated CO2 concatenation in the atmosphere on plant transpiration and water use efficiency: A study with potted carnation plants. Int. J. Biometer. 23: 343–351.
• Fiel S, Sablatnig R. 2010. Leaf classification using local features In: Proc. of 34th annual Workshop of the Austrian Association for Pattern Recognition (AAPR). 69-74.
• Hossain J, Amin MA. 2010. Leaf shape identification based plant biometrics. Proceedings of 13th International Conference on Computer and Information Technology, Dec. 23-25, IEEE Xplore Press, Dhaka, Bangladesh. 458-463.
• Huff PM, Wilf P, Azumar EJ. 2003. Digital future for paleoclimate estimation from fossil leaves? Preliminary results Palaios. 18: 266–274.
• Im C, Nishida H, Kunii TL. 1998 .Recognizing plant species by leaf shapes – a case study of the Acer family. Proc. Pattern Recog. 2 :1171-1173.
• Jensen RJ. 2003. The conundrum of morphometrics. Taxon .52: 663–671.
• Jensen RJ. 1990. Detecting shape variation in oak leaf morphology: a comparison of rotational-fit methods. Am J Bot .77: 1279-1293.
• Jonckheere I, Fleck S, Nackaerts K, Muys B, Coppin P, Weiss M, Baret F. 2004. Review of methods for in situ leaf area index determination. I: Theories, sensors and hemispherical photography. Agric. For. Meteorol. 121: 19-35.
• Juneau KJ, Tarasoff CS. 2012. Leaf area and water content changes after permanent and temporary storage. PLoS ONE 7 : e42604.
• Kandiannan K, Parthasarathy U, Krishnamurthy KS, Thankamani CK, Srinivasan V. 2009. Modelling individual leaf area of ginger (Zingiber officinale Roscoe) using leaf length and width. Scientia Horticulturae. 120:532-537.
• Kerstetter RA, Poethig RS. 1998. The specification of leaf identity during shoot development. Annu. Rev. Cell Dev. Biol. 14:373-398.
• Kumar R, Sharma S. 2010. Allometric model for non destructive leaf area estimation in clary sage (Salvia sclarea L.). Photosynthetica.48: 313-316.
• Landis DA, Isaacs R, Neal ME. 2002. An Inexpensive, Accurate method for measuring leaf area and defoliation through digital image analysis. J Econ Entomol. 95:1190-1194.
• Leith JH, Reynolds JP, Rogers HH. 1986. Estimation of leaf area of soybeans grown under elevated carbon dioxide levels. Field Crops Res. 13: 193–203.
• Lee KB, Hong KS. 2013. An implementation of leaf recognition system using leaf vein and shape. International Journal of Bio-Science and Bio- Technology. 57-66.
• Lu H, Lu Y, Wei ML, Chan LF. 2004. Comparison of different models for nondestructive leaf area estimation in taro. Agronomy Journal. 96: 448-453.
• Maas FM, Bakx EJ. 1995. light on growth and flowering of Rosa hybrida ‘Mercedes’. J. Am. Soc. Sci. 120:571–576.
• Marcus LF, Rohlf FJ, Bookstein FL. 1990. Traditional morphometrics. In Proceedings of the Michigan morphometrics workshop. Ann Arbor: University of Michigan press .227–236.
• Misle E, Kahlaoui B, Hachicha M, Alvarado P. 2013. Leaf area estimation in muskmelon by allometry,. Photosynthetica. 51:613-620.
• Morel P, Crespel L, Galopin G, Moulia B. 2012. Effect of mechanical stimulation on the growth and branching of garden rose. Sci. Hortic. 135:59-64.
• Morel P, Galopin G, Dones N. 2009. Using architectural analysis to compare the shape of two hybrid tea rose genotypes. Sci.Hortic. 120:391-398.
• Mouine S, Yahiaoui I, Yahiaoui A. 2012. Advanced shape context for plant species identification using leaf image retrieval. "ICMR '12 - 2nd ACM International Conference on Multimedia Retrieval.
• Niu G, Rodriguez DS. 2009. Growth and Physiological Responses of Four Rose Rootstocks to Drought Stress. Journal of the American Society for Horticultural Science. 134: 202-209.
• Oide M, Ninomiya S. 2000. Discrimination of soybean leaflet shape by neural networks with image input. Comput. Electron. Agric. 29:59-72.
• Oner F, Odabas MS, Sezer I, Odabas F. 2011. Leaf area prediction for corn (Zea mays L.) cultivars with multiregression analysis. Photosynthetica. 49: 637-640.
• OrsiniI F, D’urzo MP, Inan G, Serra S, OH DH, Mickelbart MV, Consiglio F. 2010. A comparative study of salt tolerance parameters in 11 wild relatives of Arabidopsis thaliana. Journal of Experimental Botany. 61: 3787 -3798.
• Ritz CM, Schmuths H, Wissemann V. 2005. Evolution by reticulation: European dogroses originated by multiple hybridization across the genus Rosa. J. Heredity. 96: 4-14.
• Rouphael Y, Cardarelli M, Ajouz N, Marucci A, Colla G. 2010. Estimation of leaf number of eggplant using thermal time model. Journal of Food Agriculture and Environment.8:847-850.
• Rouphael Y, Mouneimne AH, Ismail A, Mendoza-de Gyves E, Rivera CM. 2010. Modeling individual leaf area of rose (Rosa hybrida L.) based on leaf length and width measurement. Photosynthetica. 48: 9-15.
• Royer DL, Wilf P. 2006. Why do toothed leaves correlate with cold climates? Gas exchange at leaf margins provides new insights into a classic paleotemperature proxy. International Journal of Plant Sciences. 167: 11–18.
• Schneider CA, Rasband WS, Eliceiri KW. 2012. NIH Image to ImageJ: 25 years of image analysis . Nature Methods.9 : 671- 675.
• Silva M, Fontes P, Viana RG. 2008. Estimativa da area da folha da batateira utilizando medidas lineares. Horticultura Brasileira. 26: 083-087.
• Spalding EP, Miller ND. 2013. Image analysis is driving a renaissance in growth measurement, Curr. Opin. Plant Biol. 16:100–104.
• Spann TM, Heerema RJ. 2010. A simple method for nondestructive estimation of total shoot leaf area in tree fruit crops. Scientia Horticulturae. 125:528-533.
• Tsialtas JT, Maslaris N. 2008. Nitrogen fertilization affects on leaf morphology and evaluation of leaf area and LAI prediction models in sugar beet. Photosynthetica. 46: 346-350.
• Viscosi V, Cardini A. 2011. Leaf Morphology, Taxonomy and Geometric Morphometrics: A Simplified Protocol for Beginners. PLoS ONE 6(10): e25630.
• Waldchen J, Mader P. 2017. Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review. Archives of Computational Methods in Engineering. 1-37.
• Wang Z, Feng D. 2003. Shape based leaf image retrieval. IEE Proceedings on Vision, Image and Processing. 150:34- 43.
• Warman L, Moles AT, Edwards W. 2011. Not so simple after all: Searching for ecological advantages of compound leaves. Oikos. 120: 813 -821.
• Wu SG, Bao FS, Xu EY, Wang YX. 2007. Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network. IEEE International Symposium on Signal Processing and Information Technology.