Models for leaf area estimation in dwarf pigeon pea by leaf dimensions

Pezzini, Rafael Vieira; Filho, Alberto Cargnelutti; Alves, Bruna Mendonça; Follmann, Diego Nicolau; Kleinpaul, Jéssica Andiara; Wartha, Cleiton Antônio; Silveira, Daniela Lixinski

doi:10.1590/1678-4499.2017106

Cited by 14 publications

(15 citation statements)

References 14 publications

(14 reference statements)

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“…Among the 1,452 leaves used for generation and among the 363 leaves used for validation of the models, the CV of the length x width product (LW) of the leaf blade and leaf area (Y) was approximately twice the CV of the length (L) and width (W) of the leaf blade (Table 2). A similar pattern was observed in leaves of coccoloba (Mariano et al, 2009), snap bean (Toebe et al, 2012), forage turnip (Cargnelutti et al, 2012), pigeon pea (Cargnelutti et al, 2015), yacon (Cunya et al, 2017), sunn hemp (Carvalho et al, 2017), dwarf pigeon pea (Pezzini et al, 2018), triticale (Toebe et al, 2019), and coffee (Cavallaro et al, 2020).…”

Section: Resultssupporting

confidence: 61%

“…Alternatively, if the researcher wants to minimize the work and make only one measurement on the leaves, he/she should opt for W and for quadratic (Ŷ = -0.2884 + 1.1428x + 0.5832x 2 , R 2 = 0.9262) or power (Ŷ = 1.1536x 1.7752 , R 2 = 0.9260) models. However, these two models have a poorer fit when compared to the models generated from the LW (two measurements), but a better fit when compared to those generated from L. Models generated based on LW were recommended to estimate leaf area in species such as: coccoloba (Mariano et al, 2009), sunn hemp (Cardozo et al, 2011;Carvalho et al, 2017), snap beans (Lakitan et al, 2017), forage turnip (Cargnelutti et al, 2012), pigeon pea (Cargnelutti et al, 2015), yacon (Cunya et al, 2017), dwarf pigeon pea (Pezzini et al, 2018), triticale (Toebe et al, 2019), and coffee (Cavallaro et al, 2020 buckwheat, in which the linear model without intercept was also the most appropriate for estimating the leaf area of Coccoloba rosea (Ŷ = 0.7705x, R =0.98) and Coccoloba ramosissima (Ŷ = 0.7416x, R 2 =0.91), as a function of LW (x) (Mariano et al, 2009). For buckwheat, leaf area models were generated for the variety Hruszowska (Almehemdi et al, 2017), but with a methodology different from that used in the present study.…”

Section: Resultsmentioning

confidence: 99%

“…Precise models for leaf area estimation as a function of the linear dimensions of the leaves have been generated for species of the same family as buckwheat, such as coccoloba (Mariano, Amorim, Mariano, & Silva, 2009), in soil cover species, such as: sunn hemp (Cardozo, Parreira, Amaral, Alves, & Bianco, 2011;Carvalho, Toebe, Tartaglia, Bandeira, & Tambara, 2017), forage turnip (Cargnelutti, Toebe, Burin, Fick, & Casarotto, 2012), pigeon pea (Cargnelutti, Toebe, Alves, & Burin, 2015), and dwarf pigeon pea (Pezzini et al, 2018); and in agricultural species such as: snap beans (Lakitan, Widuri, & Meihana, 2017;, yacon (Cunya, Edquén, & Zumaeta, 2017), triticale (Toebe, Melo, Souza, Mello, & Tartaglia, 2019), and coffee (Cavallaro, Uber-Bucekb, & Finzer, 2020).…”

Section: Modelagem Trigo Sarracenomentioning

confidence: 99%

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Estimation of buckwheat leaf area by leaf dimensions

Filho¹,

Pezzini²,

Neu³

et al. 2021

Semina: Ciênc. Agrár.

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The objective of this work was to model and identify the best models for estimating the leaf area, determined by digital photos, of buckwheat (Fagopyrum esculentum Moench) of the cultivars IPR91-Baili and IPR92-Altar, as a function of length (L), width (W) or length x width product (LW) of the leaf blade. Ten uniformity trials (blank experiments) were carried out, five with IPR91-Baili cultivar and five with IPR92-Altar cultivar. The trials were performed on five sowing dates. In each trial and cultivar, expanded leaves were collected at random from the lower, middle and upper segments of the plants, totaling 1,815 leaves. In these 1,815 leaves, L and W were measured and the LW of the leaf blade was calculated, which were used as independent variables in the model. The leaf area of each leaf was determined using the digital photo method (Y), which was used as a dependent variable of the model. For each sowing date, cultivar and thirds of the plant, 80% of the leaves (1,452 leaves) were randomly separated for the generation of the models and 20% of the leaves (363 leaves) for the validation of the models of leaf area estimation as a function of linear dimensions. For buckwheat, IPR91-Baili and IPR92-Altar cultivars, the quadratic model (Ŷ = 0.5217 + 0.6581LW + 0.0004LW2, R2 = 0.9590), power model (Ŷ = 0.6809LW1.0037, R2 = 0.9587), linear model (Ŷ = 0.0653 + 0.6892LW, R2 = 0.9587) and linear model without intercept (Ŷ = 0.6907LW, R2 = 0.9587) are indicated for the estimation of leaf area determined by digital photos (Y) based on the LW of the leaf blade (x), and, preferably, the linear model without intercept can be used, due to its greater simplicity.

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Section: Resultssupporting

confidence: 61%

Section: Resultsmentioning

confidence: 99%

Section: Modelagem Trigo Sarracenomentioning

confidence: 99%

See 1 more Smart Citation

Estimation of buckwheat leaf area by leaf dimensions

Filho¹,

Pezzini²,

Neu³

et al. 2021

Semina: Ciênc. Agrár.

Self Cite

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“…Minimum, maximum, average, standard deviation and coefficient of variation in length along the midrib (L, cm), maximum width (W, cm), length of the product by the maximum width (LW, cm 2 ) and observed leaf area (OLA, cm 2 ) of Plectranthus barbatus Andrews leaves In relation to variability measured by CV, in the leaves sample used in the adjust of the regression equations, all the allometric measures (L, W, LW and OLA) showed values of CV very high (Table 1), according to Pimentel-Gomes' (2009) criteria. According to Pezzini et al (2018) the high value of CV is important for models generation, because it can be explained by the collection of leaves in several growth stages, characterizing the growth of the plants. In the 100 leaves used for validation, the variability found in the measures of L, W, LW and OLA was also considered high, according to Pimentel-Gomes' (2009) criteria, showing suitable for this type of analyze, as also stood out Schmildt, Hueso, Pinillos, Stellfeldt, and Cuevas (2017).…”

Section: Resultsmentioning

confidence: 99%

Leaf Area Estimation of Garden Boldo From Linear Dimensions

Ribeiro¹,

Mundim²,

Mendonça³

et al. 2019

JAS

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The objective of this work was to determine a mathematical equation using linear measures that allows estimating a leaf area of the specie Plectranthus barbatus Andrews, a plant with medicinal properties popularly known as garden boldo. For this was performed a direct measurement of the leaf blade considering the length (L) along the midrib and the maximum width (W) perpendicular to the midrib of 500 leaves of different specimens and the observed foliar area (OLA), which were obtained by digitized images. A regression study with linear, quadratic, potential and exponential models was performed using a random sample of 400 from the evaluated leaves using OLA as a function of L, W or LW and then obtaining the estimated leaf area (ELA) of each model. From the remaining 100 leaves a validation of the tested models was performed using ELA as a function of OLA in a simple linear regression. From the residues between ELA and OLA the root-square-mean error and Willmot index (d) was obtained and the normality was verified. The parameters used for validation were: statistically linear and angular coefficient equal to zero and one respectively; coefficient of determination closest to the unit; RQME closer to zero; d index closest to the unit; normal distribution of residues. The equation that best represents the estimated leaf area of the garden boldo is ELA = 0.1389 + 0.6779 (LW).

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“…This finding leads to the need for developing a non-destructive measurement of the total leaf area of T. paniculatum. Pezzini et al (2018) agreed that a single-trait predictor (L or W) required less labor than the use of two traits (LW). Toebe et al (2019) reported that W was better than L for LA estimation in squash.…”

Section: Propagation Of Talinum Paniculatum Using Stem Cuttingsmentioning

confidence: 89%

Lesser-known ethnic leafy vegetables Talinum paniculatum grown at tropical ecosystem: Morphological traits and non-destructive estimation of total leaf area per branch

Lakitan

Kartika²,

Widuri

et al. 2021

Biodiversitas

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Abstract. Lakitan B, Kartika K, Widuri LI, Siaga E, Fadilah LN. 2021. Lesser-known ethnic leafy vegetables Talinum paniculatum grown at tropical ecosystem: Morphological traits and non-destructive estimation of total leaf area per branch. Biodiversitas 22: 4487-4495. Talinum paniculatum known as Java ginseng is an ethnic vegetable in Indonesia that has also been utilized as a medical plant. Young leaves are the primary economic part of T. paniculatum, which can be eaten fresh or cooked. This study was focused on characterizing morphological traits of T. panicultaum and developing a non-destructive yet accurate and reliable model for predicting total area per leaf cluster on each elongated branch per flush growth cycle. The non-destructive approach allows frequent and timely measurements. In addition, the developed model can be used as guidance for deciding the time to harvest for optimum yield. Results indicated that T. paniculatum flourished rapidly under wet tropical conditions, especially if they were propagated using stem cuttings. The plants produced more than 50 branches and more than 800 leaves, or on average produced more than 15 leaves per branch at the age of nine weeks after planting (WAP). The zero-intercept linear model using a combination of two traits of length x width (LW) as a predictor was accurate and reliable for predicting a single leaf area (R2 = 0.997). Meanwhile, the estimation of total area per leaf cluster was more accurate if three traits, i.e., number of leaves, the longest leaf, and the widest leaf in each cluster were used as predictors with the zero-intercept linear regression model (R2 = 0.984). However, the use of a single trait of length (L) and width (W) of the largest leaf within each cluster as a predictor in the power regression model exhibited moderately accurate prediction at the R2 = 0.883 and 0.724, respectively.

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Models for leaf area estimation in dwarf pigeon pea by leaf dimensions

Cited by 14 publications

References 14 publications

Estimation of buckwheat leaf area by leaf dimensions

Estimation of buckwheat leaf area by leaf dimensions

Leaf Area Estimation of Garden Boldo From Linear Dimensions

Lesser-known ethnic leafy vegetables Talinum paniculatum grown at tropical ecosystem: Morphological traits and non-destructive estimation of total leaf area per branch

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