Leaf Area Estimation Model for Some Local Cherry Genotypes in Turkey

Demirsoy, Leyla; Demirsoy, Hüsnü

doi:10.3923/pjbs.2003.153.156

Cited by 12 publications

(12 citation statements)

References 2 publications

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“…Correlation coefficients calculated show that there is a close correlation between laminar area, length and width. The results obtained are consistent with those obtained in other fruit tree species as well: r2 = 0.955 for the cherry tree (Demirsoy et al 2003), r2 = 0.997 for citrus (Mazzini et al 2010), r2 = 0.99 for walnut (Keramatlou et al 2015). In conclusion, leaf characteristics vary between different genotypes analysed, a factor that was established by other authors as well (Stoppani et al 2003).…”

Section: Parameters Analysedsupporting

confidence: 91%

Determination of leaf characteristics in different medlar genotypes using the ImageJ program

Cosmulescu¹,

Scrieciu²,

Manda³

2020

Hort. Sci. (Prague)

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The size and shape of leaves can vary significantly between different genotypes within the same species and they implicitly influence plant growth and therefore productivity. The aim of this study was to compare the length, width, and surface area of leaf in nine medlar genotypes (Mespilus germanica L.) through image segmentation procedure using ImageJ software. The data indicate large variations for leaf surface area characteristics from one genotype to another, 2.12 fold for leaf surface area (22.95–48.8 cm²), 1.38 times for leaf length (8.8–12.18 cm) and 1.6 times for leaf width (3.5–5.60 cm). Leaf shape and leaf surface area vary between different genotypes analysed, and the method used can represent a good, non-destructive model of quick and reliable estimation of the medlar leaves surface area. The information obtained can be used in physiology studies, regardless of genetic material.

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Section: Parameters Analysedsupporting

confidence: 91%

Determination of leaf characteristics in different medlar genotypes using the ImageJ program

Cosmulescu¹,

Scrieciu²,

Manda³

2020

Hort. Sci. (Prague)

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“…The measured leaves were randomly selected from the greenhouse at different dates. Selected leaf samples were measured with a ruler, according to previously published methods (Schwarz and Kläring, 2001;Kumar, 2009;Vazquez-Cruz et al, 2012), and the actual leaf area was measured with a planimeter (Demirsoy and Demirsoy 2003;Demirsoy et al, 2004;Peksen, 2007;Beyhan et al, 2008). A total of 420 leaves were analyzed in the present study.…”

Section: Methodsmentioning

confidence: 99%

“…The leaf area measurements required for obtaining this information are divided into two types: direct and indirect methods (Celik and Uzun, 2002;Cristofori et al, 2007;Demirsoy, 2009). Of these methods, indirect measuring methods are low-cost methods that can be calculated using simple mathematical equations and take less time than the direct measurement methods (Gamiely et al, 1991;Demirsoy and Demirsoy, 2003;Demirsoy et al, 2004;Serdar and Demirsoy, 2006). Researchers working in the agricultural field need fast, cost efficient, reliable, and nondestructive methods (Peksen, 2007;Demirsoy, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…It estimates the leaf area from mathematical equations involving linear measurements of leaves. A mathematical model (that usually has high accuracy) can be obtained by correlating the leaf length, leaf width, and petiole length, or some combination of these variables, with the actual leaf area of a sample of leaves using regression analysis (Gamiely et al, 1991;Demirsoy and Demirsoy, 2003;Demirsoy et al, 2004;Blanco and Folegatti, 2005;Serdar and Demirsoy, 2006;Peksen, 2007;Fallovo et al, 2008;Kumar, 2009;Demirsoy and Lang, 2010). Today, there are many mathematical models for identifying the leaf areas of horticulture plants by making use of linear measurements of leaf width, length, and some combination of these variables.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, any new or improved model should be validated. There are many studies on the validation process for different plants such as tomato (Dumas, 1990;Astegiano et al, 2001;Schwarz and Kläring, 2001;Blanco and Folegatti, 2003;Beyhan et al, 2008), cucumber (Robbins andPharr, 1987;Uzun and Çelik, 1999;Blanco and Folegatti, 2005;Cho et al, 2007), pepper (Uzun and Çelik, 1999), eggplant (Uzun and Çelik, 1999;Rivera et al, 2007), watermelon (Rajendran and Thamburaj, 1987), avocado (Uzun and Çelik, 1999), red current (Uzun and Çelik, 1999), kiwifruit (Uzun and Çelik, 1999), grapes (Elsner and Jub, 1988;Uzun and Çelik, 1999), cherry (Demirsoy and Demirsoy, 2003;Demirsoy and Lang, 2010), and peach (Demirsoy et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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A modeling study with an artificial neural network: developing estimationmodels for the tomato plant leaf area

Küçükönder¹,

Boyaci²,

Akyüz³

2016

Turk J Agric For

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The leaf area measurement is an important parameter in understanding the growth and physiology of a plant. Therefore, this study aimed to develop the best leaf area estimation model for tomato plants grown in plastic greenhouse conditions. The artificial neural network (ANN) and regression analysis techniques were used in the formation of a leaf area estimation model by using the leaf width and leaf length measurements determined by the linear measurement method. The plant material for the study consisted of 420 leaf samples of the Typhoon F1 tomato type grown in plastic greenhouse conditions. In the comparison of the created models according to both methods, the criteria of selecting low values for the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE), and high value for the determination coefficient (R 2) were taken into account, and the best estimation models were determined. In the comparison made according to these criteria, it was concluded that the error values of the ANN model [R 2 = 0.96, RMSE = 3.30, MAE = 1.94, and MAPE = 0.05] were lower than those of the regression model [R 2 = 0.92, RMSE = 4.71, MAE = 3.31, and MAPE = 0.08], and that the ANN method provided a better fit to the actual values; therefore, the ANN model can be used as an alternative method in estimating the leaf area.

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Dissecting the genetic architecture of leaf morphology traits in mungbean (Vigna radiata (L.) Wizcek) using genome‐wide association study

Chiteri

Chiranjeevi

Jubery

et al. 2023

The Plant Phenome Journal

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Mungbean (Vigna radiata (L.) Wizcek) is an important pulse crop, increasingly used as a source of protein, fiber, low fat, carbohydrates, minerals, and bioactive compounds in human diets. Mungbean is a dicot plant with trifoliate leaves. The primary component of many plant functions, including photosynthesis, light interception, and canopy structure, are leaves. The objectives were to investigate leaf morphological attributes, use image analysis to extract leaf morphological traits from photos from the Iowa Mungbean Diversity (IMD) panel, create a regression model to predict leaflet area, and undertake association mapping. We collected over 5000 leaf images of the IMD panel consisting of 484 accessions over 2 years (2020 and 2021) with two replications per experiment. Leaf traits were extracted using image analysis, analyzed, and used for association mapping. Morphological diversity included leaflet type (oval or lobed), leaflet size (small, medium, large), lobed angle (shallow, deep), and vein coloration (green, purple). A regression model was developed to predict each ovate leaflet's area (adjusted R2 = 0.97; residual standard errors of < = 1.10). The candidate genes Vradi01g07560, Vradi05g01240, Vradi02g05730, and Vradi03g00440 are associated with multiple traits (length, width, perimeter, and area) across the leaflets (left, terminal, and right). These are suitable candidate genes for further investigation in their role in leaf development, growth, and function. Future studies will be needed to correlate the observed traits discussed here with yield or important agronomic traits for use as phenotypic or genotypic markers in marker‐aided selection methods for mungbean crop improvement.

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Leaf Area Estimation Model for Some Local Cherry Genotypes in Turkey

Cited by 12 publications

References 2 publications

Determination of leaf characteristics in different medlar genotypes using the ImageJ program

Determination of leaf characteristics in different medlar genotypes using the ImageJ program

A modeling study with an artificial neural network: developing estimationmodels for the tomato plant leaf area

Dissecting the genetic architecture of leaf morphology traits in mungbean (Vigna radiata (L.) Wizcek) using genome‐wide association study

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