Leaf Area Estimation in Litchi by Means of Allometric Relationships

Oliveira, Pablo Souto; Silva, Wilton; Costa, Adriana Aparecida Matta; Schmildt, Edílson Romais; Vitória, Edney Leandro da

doi:10.1590/0100-29452017403

Cited by 18 publications

(24 citation statements)

References 9 publications

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“…The region is characterized by tropical AW (tropical humid) climate, with rainfall in summer and dry winter according to the classification of köppen [15]. 500 leaves of 12 different plants were counted, and in each sampled plant, leaves were collected at all stages of development at the four cardinal points, which did not present damage or attack of diseases or pests, as suggested by Oliveira et al [13]. The leaves were packed in plastic bags and transported to the Plant Improvement Laboratory of the University of Espírito Santo (CEUNES).…”

Section: Methodsmentioning

confidence: 99%

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Mathematical Modeling to Estimation Leaf Area of Pimenta dioica from Linear Dimensions

Oliveira¹,

Gonçalves²,

Costa³

et al. 2019

IJPSS

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The objective of this work was to obtain regression equations and to indicate the most appropriate from different mathematical models for the estimation of the leaf area of Allspice (Pimenta dioica) by non - destructive method. 500 leaves of plants located in the municipality of São Mateus, North of Espírito Santo State, Brazil, were collected, 400 of which were used to adjust the equations and 100 for validation. The length (L) along the main midrib, the maximum width (W), the product of the length with the width (LW) and the observed leaf area (OLA) were measured from all leaves. We fitted models of linear equations of first degree, quadratic and power, where OLA was the dependent variable in function of L, W and LW. From the 100 sheets intended for validation, and using the adjusted equations for each mathematical model, the estimated leaf area (ELA) was obtained. Subsequently, a simple linear regression was fitted for each model of the proposed equation in which ELA was the dependent variable and OLA the independent variable. The mean absolute error (MAE), the root mean square error (RMSE) and Willmott's index d also determined. The best fit had as selection criterion the non-significance of the comparative means of ELA and OLA, MAE and RMSE values closer to zero and value of the coefficient of determination coefficient (R2) close to one. Thus, the power model (ELA = 0.7605(LW)0.9926, R2 = 0.9764, MAE = 1.0066, RMSE = 1.7759 and d = 0.9950) based on the product of length and width (LW) is the most appropriate for estimating the leaf area of Pimenta dioica.

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Section: Methodsmentioning

confidence: 99%

“…[8], Vitis vinífera L. [10], Coffea canephora [9], Crotalaria juncea [5], Litchi chinensis Sonn. [13], Prunus armeniaca L. [14] and Annona cherimila Mill. [12] indicated that the product of the combination of length and width is more predictive in estimating the leaf area of these species.…”

Section: Ssionmentioning

confidence: 99%

Mathematical Modeling to Estimation Leaf Area of Pimenta dioica from Linear Dimensions

Oliveira¹,

Gonçalves²,

Costa³

et al. 2019

IJPSS

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“…Note in Figure 3 that models involving a single measurement were less acceptable in the C. rosea leaf area estimate due to its lower R 2 values. In addition, the equations based on L and W individually presented MAE and RMSE values that were farther from zero and values of the Willmott d index more distinct to one, when compared the models using LW (Table 3), these criteria should be used to select the model that best fits the leaf area estimate (Oliveira et al, 2017). It was also verified that the comparative means of OLA and ELA did not statistically differ by Student's t test at 5% of probability in all proposed models (Table 3), attesting a good similarity of the means of OLA and ELA.…”

Section: Methodsmentioning

confidence: 99%

“…In this way, are notorious the great number of works involving mathematical equations as non-destructive method to estimate the leaf area in a wide range of vegetal species as Cucumis sativus L. (Cho, S. Oh, M. M. Oh, & Son, 2007), Helianthus annuus L. (Rouphael, Colla, Fanasca, & Karam, 2007), Jatropha curcas (Pompelli et al, 2012), Vitis vinífera L. (Buttaro, Rouphael, Rivera, Colla, & Gonnella, 2015), Coffea canephora (Schmildt, Amaral, Santos, & Schmildt, 2015), Rosa hybrida L. (Costa, Pôças, & Cunha, 2016), Crotalaria juncea (Carvalho et al, 2017), Prunus armeniaca L. (Cirillo et al, 2017), Litchi chinensis Sonn (Oliveira, Silva, Costa, Schmildt, & Vitória, 2017), guava (Vitória et al, 2018), Carica papaya L. (Oliveira et al, 2019) and Plectranthus barbatus Andrews (Ribeiro et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Estimation Leaf Area by Composite Leaves of Canavalia rosea Seedlings Through Linear Dimensions From Last Leaflet

Pinheiro¹,

Oliveira²,

Santos³

et al. 2019

JAS

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The objective of this work was to propose models of equations from measurements of the linear dimensions of the last leaflet for the estimation of the leaf area of the composite leaves of Canavalia rosea. For this purpose, 441 composite leaves of 198 seedlings were used, 45 days after sowing, produced in nursery and belonging to the Federal University of Espírito Santo, Campus São Mateus, located in the municipality of São Mateus, North of the State of Espírito Santo, Brazil. The length (L) along the main midrib and the maximum leaf width (W) of the last leaflet of each composite leaf, as well as the leaf area of all leaflets, were measured. Subsequently, it was determined the product of the multiplication of the length with the width (LW) and leaf area observed (OLA) from the sum of leaf area of leaflets in front of these measures were adjusted linear and non-linear equations of linear first degree, quadratic and power models, where, OLA was used as a dependent variable in function of L, W and LW as independent variable. Based on the models tested, we obtained equations for the estimated leaf area (ELA). The mean values of ELA and OLA were compared by Student's t test 5% probability. The mean absolute error (MAE), the root mean square error (RMSE) and the Willmott d index, were determined as criteria for validation. The best adjusted equation was chosen through the non-significant values in the comparison of the means of ELA and OLA, values of MAE and RMSE closer to zero, value of the index d near the unitary and higher values of R2. Thus, the leaf area of the composite leaf of C. rosea seedlings can be estimated by the power model represented by equation ELA = 2.2951 (LW)0.9474 quickly, easily and non-destructively.

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“…The use of regression models has become a very precise non-destructive method that allows the real leaf area of plants to be estimated from linear measurements of leaf blades (length and width), which are directly correlated with leaf surface area (ZHANG; PAN, 2011). Such methods have been used to estimate leaf area of both cultivated (GANESHAMURTHY et al, 2016;CARVALHO et al, 2017;OLIVEIRA et al, 2017) and forest species (ASSIS et al, 2015;KERAMATLOU et al, 2015;RIBEIRO et al, 2018b;RIBEIRO et al, 2019a;RIBEIRO et al, 2019b).…”

Section: Introductionmentioning

confidence: 99%

A NON-DESTRUCTIVE METHOD FOR ESTIMATING LEAF AREA OF Ceiba glaziovii (Kuntze) K. Schum.

Ribeiro

Figueiredo

Coêlho

et al. 2019

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The determination of leaf area is of fundamental importance in studies involving ecological and ecophysiological aspects of forest species. The objective of this research was to adjust an equation to determine the leaf area of Ceiba glaziovii as a function of linear measurements of leaves. Six hundred healthy leaf limbs were collected in different matrices, with different shapes and sizes, in the Mata do Pau-Ferro State Park, Areia, Paraíba state, Northeast Brazil. The maximum length (L), maximum width (W), product between length and width (L.W), and leaf area of the leaf limbs were calculated. The regression models used to construct equations were: linear, linear without intercept, quadratic, cubic, power and exponential. The criteria for choosing the best equation were based on the coefficient of determination (R²), Akaike information criterion (AIC), root mean square error (RMSE), Willmott concordance index (d) and BIAS index. All the proposed equations satisfactorily estimate the leaf area of C. glaziovii, due to their high determination coefficients (R² ≥ 0.851). The linear model without intercept, using the product between length and width (L.W), presented the best criteria to estimate the leaf area of the species, using the equation 0.4549*LW.

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Leaf Area Estimation in Litchi by Means of Allometric Relationships

Cited by 18 publications

References 9 publications

Mathematical Modeling to Estimation Leaf Area of Pimenta dioica from Linear Dimensions

Mathematical Modeling to Estimation Leaf Area of Pimenta dioica from Linear Dimensions

Estimation Leaf Area by Composite Leaves of Canavalia rosea Seedlings Through Linear Dimensions From Last Leaflet

A NON-DESTRUCTIVE METHOD FOR ESTIMATING LEAF AREA OF Ceiba glaziovii (Kuntze) K. Schum.

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