Leaf shape simulation of castor bean and its application in nondestructive leaf area estimation

Hailin Wei, Xumeng Li, Ming Li, Huang Huang

Abstract


The leaf shape and leaf area measurement are crucial in plant growth modeling. The castor bean leaf is large, palm-shaped with multiple clefts. The leaf shape simulation and leaf area estimation were less studied. The circular model and nonrectangular hyperbolic model were developed to describe the standard leaf shape of castor bean in this study, providing a model for simulating the leaf shape and a nondestructive way for estimating the leaf area respectively. In addition, a formula was established to estimate the leaf area by the parameter of the standard leaf shape of castor bean. Based on validation results, the circular model fits the landmarks and nonrectangular hyperbolic model fits the lobe margins very well. The leaf area was accurately estimated by using the established formula. This study could provide a theoretical reference for leaf visualization, a nondestructive and easy way to estimate the leaf area for other complex leaves with multiple lobes.
Keywords: leaf shape, leaf area, geometrical characteristics, mathematical model, image processing, plant growth modeling
DOI: 10.25165/j.ijabe.20191204.4040

Citation: Wei H L, Li X M, Li M, Huang H. Leaf shape simulation of castor bean and its application in nondestructive leaf area estimation. Int J Agric & Biol Eng, 2019; 12(4): 135–140.

Keywords


leaf shape, leaf area, geometrical characteristics, mathematical model, image processing, plant growth modeling

Full Text:

PDF

References


Liebig H P. Modelling potential crop growth processes. Current Issues in Production Ecology, 1994; 2: 131–142.

Thomson J A. On growth and form. Nature, 1917; 100: 21–22

O’Shea B, Mordue-Luntz A J, Fryer R J, Pert C C, Bricknell I R. Determination of the surface area of a fish. J. Fish Dis., 2006; 29(7): 437–440.

Montgomery E G. Correlation studies in corn. Annual Report No. 24; Nebraska Agricultural Experimental Station: Lincoln, NB, USA, 1911; pp.108–159.

Shi P J, Zheng X, Ratkowsky D A, Li Y, Wang P, Cheng L. A simple method for measuring the bilateral symmetry of leaves. Symmetry, 2018; 10(4): 118.

Palaniswamy K M, Gomez K A. Length-width method for estimating leaf area of rice. Agron. J., 1974; 66(3): 430–433.

Verwijst T, Wen D Z. Leaf allometry of Salix viminalis during the first growing season. Tree Physiol., 1996; 16(7): 655–660.

Shi P J, Liu M D, Yu X J, Gielis J, Ratkowsky D A. Proportional relationship between leaf area and the product of leaf length width of four types of special leaf shapes. Forests, 2019; 10(2): 178.

Shi P J, Liu M D, Ratkowsky D A, Gielis J, Su J L, Yu X J, et al. Leaf area–length allometry and its implications in leaf shape evolution. Trees,

; pp.1–13. Springer. https:doi.org/10.1007/s00468-019-01843-4.

Dornbusch T, Watt J, Baccar R, Fournier C, Andrieu B. A comparative analysis of leaf shape of wheat, barley and maize using an empirical shape model. Ann. Bot., 2011; 107(5): 865–873.

Gielis J. A generic geometric transformation that unifies a wide range of natural and abstract shapes. Am. J. Bot., 2003; 90(3): 333–338.

Shi P J, Xu Q, Sandhu H S, Gielis J, Ding Y L, Li H R, et al. Comparison of dwarf bamboos (Indocalamus sp.) leaf parameters to determine relationship between spatial density of plants and total leaf area per plant. Ecol. Evol., 2015; 5(20): 4578–4589.

Shi P J, Ratkowsky D A, Li Y, Zhang L F, Lin S Y, Gielis J. General leaf-area geometric formula exists for plants—Evidence from the simplified Gielis equation. Forests, 2018; 9(11): 714.

Jani T C, Misra D K. Leaf area estimation by linear measurements in Ricinus communis. Nature, 1966; 212(5063): 741–742.

Li X M, Wang X H, Wei H L, Zhu X G, Peng Y L, Li M, et al. A technique system for the measurement, reconstruction and character extraction of rice plant architecture. Plos One, 2017; 12(5): e0177205.

Zhang X. Studies on the measurement of plant leaf area based on image processing. Harbin: Northeastern Agricultural University, 2009. (in Chinese)

Rosin P L. Image difference threshold strategies and shadow detection. BMVC, 1995; 95: 347–356.

Gonzalez R C , Woods R E , Eddins S L. Digital image processing using Matlab. Publishing House of Electronics Industry, 2009.

Haralick, R M, Shapiro L G. Computer and robot vision, Volume I, Addison-Wesley, 1992; pp.28-48.

Salazar-Colores S, Ramos-Arreguín J M, Echeverri C J O, et al. Image dehazing using morphological opening, dilation and Gaussian filtering. Signal, Image Video P., 2018; 12(7): 1329–1335.

Annaldas B. Multi-parametric MRI study of brain insults (Traumatic brain injury and brain tumor) in animal models. Tempe: Arizona State University, 2014.

Kruczyk M, Umer H M, Enroth S, Komorowski J. Peak finder metaserver - a novel application for finding peaks in ChIP-seq data. BMC Bioinformatics, 2013; 14(1): 280.

Pierre S. Morphological image analysis: Principles and applications. Sensor Review, 1999; 28(5): 800–801.

Li X M, Wang X H, Huang H, Li X P. A cereal crop canopy light distribution and photosynthesis model based on multiple factors - modeling and simulation. Pak. J. Bot., 2014; 46(3): 927–938.

Ji L Q. A modified Logistic model for forecasting petroleum consumption in China. Journal of China University of Petroleum, 2011; 35(4): 177–181. (in Chinses)

Rinaldi M, Losavio N, Flagella Z. Evaluation and application of the OILCROP–SUN model for sunflower in southern Italy. Agr. Syst., 2003; 78(1): 17–30.

Seber G A F, Wild C J. Nonlinear regression. Hoboken. New Jersey: John Wiley & Sons, Inc., 2003; pp.62–63.

Gong A P, Wu W H, Qiu Z J, He Y. Leaf area measurement using Android OS mobile phone. Transactions of the CSAM, 2013; 44(9): 203–208. (in Chinese)




Copyright (c) 2019 International Journal of Agricultural and Biological Engineering



2023-2026 Copyright IJABE Editing and Publishing Office