Non-destructive method for estimating leaf area of Ocimum gratissimum L. using leaf dimensions

ABSTRACT

Basil (Ocimum gratissimum - Lamiaceae), is a sub-shrub plant species with great economic importance for several regions, and studies on its growth, physiology, and reproduction become needed. The aimed research was to obtain a regression equation to estimate leaf area of O. gratissimum. 250 basil leaves were collected and the linear dimensions (length and width) and real leaf area of each leaf were measured. From these data, the products between length and width, length and length were calculated. Equations were obtained using regression models: linear, linear without intercept, quadratic, cubic, power, and exponential. The best equation was selected based on determination coefficient, Pearson’s correlation coefficient, Willmott’s agreement index and CS index, Akaike information criterion, mean absolute error and root mean square error. All equations proposed using the product between length and width (LW) can be used to predict the leaf area of O. gratissimum. However, the equation LA=0.54*LW^1.03 (power model) is the most recommended to estimate the leaf area of this species.

Keywords
allometric equations; basil; lamiaceae; leaf blades; regression models

INTRODUCTION

Ocimum gratissimum L. (Lamiaceae), popularly known as “basilicão”, “alfavacão”, “alfavaca”, “alfavaca-de-vaqueiro” and “manjericão-cheiroso”, is a sub-shrub plant species native to Asia and Africa, naturalized in America, and occurring in all regions of Brazil (Grin-Global, 2016; Antar, 2020). In addition to its potential source of essential oils used in the perfume, cosmetics, and pharmaceutical industries, the plant shows antibacterial and antifungal properties (Cruz & Bezerra, 2017). It is widely used in culinary and medicinal purposes for the treatment of several diseases such as cancer, inflammation, urinary tract, gastrointestinal infections, cholesterol, influenza, and secretions (Bitu et al., 2015; Santana et al., 2016; Oyelakin et al., 2020). Also, the plant was proven to have diuretic, hypoglycemic, antimicrobial, and antioxidant activities (Akpan et al., 2014; Hzounda et al., 2016; Monga et al, 2017; Monteiro et al., 2021).

Due to the importance of the species, studies related to its growth, production, physiology and reproduction are of great relevance. Leaf rea measurement is one of the most important analyzes since leaves hold numerous functions, such as light interception and absorption, net CO₂ assimilation, evapotranspiration, stomatal opening and closing, and biomass accumulation (Taiz et al., 2017).

Leaf area can be measured by methods classified as direct and indirect, destructive and non-destructive (Marshall, 1968; Peksen, 2007; Sousa et al., 2015). Destructive methods are simple and precise, but they are laborious besides leading to the destruction of all the plant biomass, making long-term research unfeasible (Mota et al., 2014). Non-destructive methods based on leaf area estimation through regression equations, provide precise and fast analyzes in addition to allowing successive evaluations of plants at different growth stages (Ribeiro et al., 2020a; Santos et al., 2021).

Regression equations from linear dimensions of leaf blades have been used to prediction leaf area of other plant species belonging to the same botanical family of O. gratissimum, such as Tectona grandis Linn. f. (Tondjo et al., 2015), Plectranthus ornatos Codd. (Silva et al., 2017), Mentha piperita L. (Daramola et al., 2018), Mesosphaerum suaveolens (L.) Kuntze (Ribeiro et al., 2020a), Sesamum indicum L. (Ribeiro et al., 2023a), Dendranthema grandiflora Tzevele (Silva et al., 2023), Ocimum basilicum L., Mentha spp. e Salvia spp. (Teobaldelli et al., 2020), and Salvia hispanica L. (Goergen et al., 2021). Likewise, this work aimed to obtain a regression equation to estimate leaf area of O. gratissimum through linear dimensions of leaves.

MATERIAL AND METHODS

The experiment was carried out under a greenhouse at the Federal University of Paraíba, municipality Areia, Paraíba state, Northeastern Brazil. The climate of the region is classified as As that is tropical with rains during the summer (Alvares et al., 2013). Altitude ranges from 400 to 600 m, annual rainfall is around 1,400 mm, and temperature of 22 °C (Ribeiro et al., 2018a). The average temperature inside the greenhouse was 28.5 °C and air relative humidity was 54% during the experiment, which was monitored using a digital thermo-hygrometer.

Basil seeds were sown in plastic pots with 5 dm³ capacity, filled with a substrate composed of vegetable soil and cattle manure. The substrate had the following chemical attributes: 6.3 pH (H₂O); 10.5 and 294.6 mg dm^-3 of P and K⁺, respectively; 0.22, 3.2, 0.72, 2.8, 1.48, 5.8, and 5.7 cmolc dm^-3 of Na⁺, H⁺+Al⁺³, Al³⁺, Ca²⁺, Mg²⁺, bases sum, and cation exchange capacity, respectively; and 28.7% organic matter.

At 150 days after sowing, 250 leaf blades were randomly collected from the middle, lower and upper thirds of each plant, selecting healthy leaves, without damages caused by biotic and abiotic factors. The leaves were transported to the Laboratory of Plant Ecology Laboratory at the Federal University of Paraíba, Areia, Paraíba state, Brazil. Length (L) and width (W) of each leaf blade were measured using a millimetric ruler (Figure 1). The product of length by width (LW), length by length (LL), and width by width (WW) were calculated. Also, real leaf area (LA) was determined through digital images. For this, each leaf was scanned in a flatbed scanner (380 model, Epson) and the images were processed individually using the ImageJ^® software (Ribeiro et al., 2018b).

Figure 1
Linear dimensions [length (L) and width (W)] used to estimate the leaf area of Ocimum gratissimum.

A descriptive analysis was performed with data, calculating maximum and minimum values, mean, amplitude, median, standard deviation, standard error, coefficient of variation, asymmetry, and kurtosis coefficient. To determine the most suitable equation to estimate basil leaf area (LA) as a function of linear dimensions of leaves, equations were adjusted using the linear, linear without intercept, quadratic, cubic, power, and exponential regression models.

The best equation was selected following the criteria: determination coefficient (R²) (Equation 1), Pearson’s linear correlation coefficient (r), Willmott agreement index (d) (Willmott et al., 1981) (Equation 2), and CS index (Camargo & Sentelhas, 1997) (Equation 3) closestto one; Akaike information criterion (AIC) (Akaike, 1974) (Equation 4), mean absolute error (MAE) (Equation 5), and root mean square error (RMSE) (Janssen & Heuberger, 1995) (Equation 6) closestto zero. Statistical analyses were performed in R software v.4.0.2 (R Core Team 2020).

$${\mathrm{R}}^2=1-\frac{\underset{i=1}{\overset{n}{\Sigma }} {\left(y_{\mathrm{i}}-{\widehat{y}}_{\mathrm{i}}\right)}^2}{\underset{i=1}{\overset{n}{\Sigma }} {\left(y_{\mathrm{i}}^{\mathrm{\prime }}\right)}^2}$$ (1)

$$d=1-\frac{\underset{i=1}{\overset{n}{\Sigma }} {\left({\widehat{y}}_{\mathrm{i}}-y_{\mathrm{i}}\right)}^2}{\underset{i=1}{\overset{n}{\Sigma }} {\left(\left|{\widehat{y}}_{\mathrm{i}}^{\mathrm{\prime }}\right|+\left|y_{\mathrm{i}}^{\mathrm{\prime }}\right|\right)}^2}$$ (2)

$$CS=r\times d$$ (3)

$$AIC=-2\ln L\left(x\mathrm{\setminus }\widehat{\theta }\right)+2\left(p\right)$$ (4)

$$MAE=\frac{\underset{i=1}{\overset{n}{\Sigma }} |\widehat{y}i-yi|}{n}$$ (5)

$$RMSE=\sqrt{\frac{\underset{i=1}{\overset{n}{\Sigma }} (\widehat{y}i-yi{)}^2}{n}}$$ (6)

where: ŷ_i: estimated leaf area; y_i: observed leaf area; ȳ_i: mean of observed values; ŷ´_i= ŷ_i – ȳ; y´_i= y_i – ȳ; L(x\ θ): maximum likelihood function, defined as the product of density function; p: number of model parameters; and n: number of observations.

RESULTS

Descriptive analysis of L, W, LW, LL, WW, and LA of 250 leaf blades of basil is shown in Table 1. L ranged from 0.645 to 16.349 cm, 6.887 cm on average, while W varied from 0.259 to 9.244 cm, 3.412 cm on average. In turn, LW was 3.412 cm² on average, varying from 0.416 to 267.290 cm²; LL was 15.677 cm² on average, ranging from 0.067 to 85.452 cm²; and WW was 31.403 cm² on average, with values from 0.167 to 147.802 cm². LA ranged from 0.098 to 91.503 cm², 18.999 cm² on average (Table 1).

Regarding data variability, the linear dimensions L and W showed the lowest coefficients of variation, 58.28 and 58.98% respectively, while the highest coefficients of variation were found for the LW (101.8%), LL (106.25%), WW (103.39%), and LA (104.32%) (Table 1). Also, L and W showed the lowest coefficients of asymmetry and kurtosis (L: 0.460 and 2.195; W: 0.577 and 2.553) as compared to LW (1.204 and 3.624), LL (1.518 and 5.152), WW (1.325 and 4.169) and LA (1.437 and 4.673) that presented high values for these coefficients (Table 1).

Table 1
Descriptive statistics on Ocimum gratissimum leaf data

Descriptive statistics	L	W	LW	LL	WW	LA
Minimum	0.645	0.259	0.416	0.067	0.167	0.098
Maximum	16.349	9.244	267.290	85.452	147.802	91.503
Amplitude	15.704	8.985	266.874	85.385	147.635	91.405
Mean	6.887	3.412	63.475	15.677	31.403	18.999
Median	6.211	2.986	38.577	8.916	18.493	11.112
Variance	16.110	4.051	4175.512	277.445	1054.235	392.790
Standard deviation	4.014	2.013	64.618	16.657	32.469	19.819
Standard error	0.264	0.132	4.252	1.096	2.136	1.304
CV (%)	58.28	58.98	101.8	106.25	103.39	104.32
Assimmetry a	0.460	0.577	1.204	1.518	1.325	1.437
Kurtosis + 3 b	2.195	2.553	3.624	5.152	4.169	4.673

• ^a
  Asymmetry differs from zero by the t-test at 5% probability;
• ^b
  Kurtosis differs from three by the t-test at 5% probability;

Scatterplots between L, W, LW, LL, WW, and LA indicated different patterns suggesting adjustments to linear and non-linear models (Figure 2). There was linear relationship between LW and LA, LL and LA, and WW and LA, and non-linear between L and LA, and W and LA (Figure 2).

Figure 2
Histogram and scatter plots between leaves dimensions [length, width (W), product of length by width (LW), product of length by length (LL), product of width by width (WW)], and real leaf area (LA) of 250 Ocimum gratissimum leaves.

Regarding percentage distribution of LA size classes of 250 basil leaves, it was found that 47.18% of the leaf area was between 0.50 and 10.00 cm², and 22.07% was between 30.01 and 92.00 cm², showing that most of the leaves in this species are small (Figure 3).

Figure 3
Percentage of real leaf area size classes of 250 Ocimum gratissimum leaves.

Table 2 shows the regression models and equations obtained from the relationship between the leaf linear dimensions and real leaf area. Power model, adjusted using with the product of length by width showed the highest R² (0.9974), r (0.9980), d (0.9990), and CS index (0.9969), and the lowest AIC (773.8), MAE (0.853), and RMSE (1.264) (Table 2).

Table 2
Regression and equations, determination coefficient (R²), Pearson’s correlation coefficient (r), Willmott agreement index (d), CS index (CS), Akaike information criterion (AIC), mean absolute error (MAE), and root mean square error (RMSE) of 250 Ocimum gratissimum leaves

Model	x	R2	r	d	CS	AIC	MAE	RMSE	Equation
Linear	L	0.9023	0.9501	0.9738	0.9252	1502.1	4.597	6.168	LA = - 13.31 + 4.69*L
Linear	W	0.9245	0.9617	0.9801	0.9425	1442.7	4.267	5.424	LA = - 13.31 + 9.47*W
Linear	LW	0.9950	0.9975	0.9987	0.9963	815.9	0.905	1.397	LA = - 0.12 + 0.61*LW
Linear (0.0)	LW	0.9958	0.9975	0.9987	0.9962	814.8	0.880	1.399	LA = 0.61*LW
Linear	LL	0.9780	0.9890	0.9944	0.9834	1158.2	1.794	2.930	LA = - 0.25 + 0.30*LL
Linear	WW	0.9828	0.9914	0.9957	0.9871	1100.4	1.630	2.585	LA = 0.50 + 1.18*WW
Quadratic	L	0.9791	0.9896	0.9947	0.9844	1147.3	1.839	2.849	LA = 1.87 - 0.69*L + 0.34*L2
Quadratic	W	0.9833	0.9917	0.9958	0.9875	1095.0	1.609	2.545	LA = - 0.85 + 0.85*W + 1.08*W2
Quadratic	LW	0.9954	0.9977	0.9988	0.9965	801.2	0.919	1.353	LA = 0.46 + 0.56*LW + 0.0004*LW2
Quadratic	LL	0.9811	0.9906	0.9952	0.9858	1124.2	1.774	2.710	LA = 0.93 + 0.25*LL + 0.0002*LL2
Quadratic	WW	0.9838	0.9919	0.9959	0.9879	1088.0	1.574	2.506	LA = - 0.11 + 1.28*WW - 0.002*WW2
Cubic	L	0.9816	0.9909	0.9954	0.9863	1118.2	1.727	2.664	LA = - 2.42 + 1.87*L - 0.03*L2 + 0.01*L3
Cubic	W	0.9837	0.9919	0.9959	0.9879	1090.5	1.578	2.509	LA = 0.68 - 0.91*W + 1.58*W2 - 0.04*W3
Cubic	LW	0.9959	0.9979	0.9990	0.9969	777.4	0.898	1.279	LA = 0.22 + 0.59*LW - 0.0003*LW2 + 0.000004*LW3
Cubic	LL	0.9824	0.9913	0.9956	0.9869	1107.6	1.668	2.603	LA = 0.01 + 0.32*LL - 0.0006*LL2 + 0.000002*LL3
Cubic	WW	0.9841	0.9921	0.9960	0.9882	1084.0	1.567	2.474	LA = 0.33 + 1.15*WW + 0.003*WW2 - 0.00004*WW3
Power	L	0.9795	0.9897	0.9948	0.9846	1145.4	1.826	2.850	LA = 0.23 * L2.11
Power	W	0.9836	0.9918	0.9958	0.9876	1091.8	1.591	2.537	LA = 1.39 * W1.92
Power	LW	0.9974	0.9980	0.9990	0.9969	773.8	0.853	1.264	LA = 0.54 * LW1.03
Power	LL	0.9796	0.9897	0.9948	0.9846	1145.4	1.826	2.850	LA = 0.23 * LL1.05
Power	WW	0.9836	0.9918	0.9958	0.9876	1091.8	1.591	2.538	LA = 1.39 * WW0.96
Exponential	L	0.9695	0.9846	0.9911	0.9758	1255.6	2.959	3.617	LA = 3.35 * 1.23L
Exponential	W	0.9428	0.9710	0.9815	0.9531	1412.0	3.947	5.075	LA = 4.56 * 1.42W
Exponential	LW	0.9428	0.9710	0.9815	0.9531	1556.5	3.947	5.075	LA = 9.77 * 1.02LW
Exponential	LL	0.8896	0.9432	0.9633	0.9086	1513.8	6.001	6.939	LA = 8.89 * 1.01LL
Exponential	WW	0.9062	0.9519	0.9704	0.9238	1667.7	5.408	6.327	LA = 11.20 * 1.03WW

Therefore, the equation LA = 0.54*LW^1.03 is the most suitable for prediction basil leaf area through dimensions of leaves, since there was low data dispersion to the model fit (R² = 0.9974) (Figure 4A). The leaf area estimated by the indicated equation had a high positive correlation with the actual leaf area, with a high determination coefficient (R² = 0.9958) (Figure 4B).

Figure 4
(A) Real leaf area and product of length by width by the proposed equation for estimating Ocimum gratissimum leaf area. (B) Relationship between real leaf area and leaf area estimated by the proposed equation (LA = 0.54*LW^1.03).

DISCUSSION

Leaf linear dimensions (length and width) showed less variability than the LW, LL, WW, and LA. High data variability is important for generating regression models aimed at estimating leaf area using linear dimensions of leaves, allowing multiple analyzes in different plants developmental stages. Therefore, the number of samples (250 leaves) used in this study was sufficient to build allometric equations to estimate the basil leaf area. High variation in LW, LL, WW, and LA were also recorded in other studies (Macário et al., 2020; Donato et al., 2020; Ribeiro et al., 2020b; Toebe et al., 2021).

Scatter plots fitted between the analyzed variables showed linear and non-linear relationships, which was observed by other studies (Carvalho et al., 2017; Cargnelutti Filho et al., 2021).

The determination coefficients (R²) of the equations were above 0.88, showing that at least 88% of the variations in basil leaf area were explained by the models obtained through linear dimensions. As compared to the equations fitted using L or W, those equations adjusted using the LW showed the best criteria for estimating leaf area (Bezerra et al., 2020; Cargnelutti Filho et al., 2021; Lucena et al., 2021; Toebe et al., 2021), except for the exponential, which showed best indexes when using leaf length (L).

The power model using LW was most suitable to estimate leaf area of other species, such as Urochloa mosambicensis (LA = LW^0.968) (Leite et al., 2017), Erythroxylum citrifolium (LA = 0.5966*LW^1.0181) (Ribeiro et al., 2019a), Psychotria carthagenensis (LA = 0.6373*LW^0.9804), Psychotria hoffmannseggiana (LA = 0.6235*LW^0.9712) (Ribeiro et al., 2019b), Psychotria colorata (Ribeiro et al., 2021), Arachis hypogaea (Ribeiro et al., 2022a), Ocimum basilicum (Ribeiro et al., 2022b), Erythrina velutina (Ribeiro et al., 2022c), and Manilkara zapota (Ribeiro et al., 2023b).

CONCLUSIONS

The equations proposed using the LW can be used to estimate the leaf area of O. gratissimum.

The equation LA = 0.54*LW1.03 (power model) is the most suitable to meaningfully estimate leaf area of O. gratissimum.

ACKNOWLEDGEMENTS, FINANCIAL SUPPORT AND FULL DISCLOSURE

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

REFERENCES

• Akaike H (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19:716-723.
• Akpan OU, Bassey RB, Agba BS & Edegha IA (2014) Elevation of serum pancreatic amylase and distortion of pancreatic cytoarchitecture in type 1 diabetes mellitus rats treated with Ocimum gratissimum Nigeria Medical Association, 55:34-38.
• Alvares CA, Stape JL, Sentelhas PC, Gonçalves JLM & Sparovek Gerd (2013) Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22:711-728.
• Antar GM (2020) Ocimum in Flora do Brasil 2020. Available at: <http://floradobrasil.jbrj.gov.br/reflora/floradobrasil/FB23332>. Accessed on: April 23^th, 2022.
  » http://floradobrasil.jbrj.gov.br/reflora/floradobrasil/FB23332
• Bezerra RCA, Leite MLMV, Almeida MCR, Lucena LRR, Simões VJLP & Sales AT (2020) Estimativa de área da lâmina foliar de Digitaria pentzii sob diferentes alturas de corte. Ciência Animal Brasileira, 21:01-15.
• Bitu VCN, Bitu VCN, Matias EFF, Lima WP, Portelo AC, Coutinho HDM & Menezes IRA (2015) Ethnopharmacological study of plants sold for therapeutic purposes in public markets in Northeast Brazil. Journal of Ethnopharmacology, 172:265-272.
• Camargo AP & Sentelhas PC (1997) Avaliação do desempenho de diferentes métodos de estimativa da evapotranspiração potencial no estado de São Paulo, Brasil. Revista Brasileira de Agrometeorologia, 5:89-97.
• Cargnelutti Filho A, Pezzini RV, Neu IMM & Dumke GE (2021) Estimation of buckwheat leaf area by leaf dimensions. Semina: Ciências Agrárias, 42:1529-1548.
• Carvalho JO, Toebe M, Tartaglia FL, Bandeira CT & Tambara AL (2017) Leaf area estimation from linear measurements in different ages of Crotalaria juncea plants. Anais da Academia Brasileira de Ciências, 89:1851-1868.
• Cruz MJF & Bezerra SB (2017) Obtenção do óleo essencial de Ocimum gratissimum L. para desenvolvimento de cosmético de limpeza facial. Revista Diálogos Acadêmicos, 6:159-162.
• Daramola OS, Olasantan FO, Salau AW, Olorunmaiye PM, Adigun JA, Joseph-Adekunle TT & Osipitan OA (2018) Rapid leaf area measurement methods for peppermint (Mentha piperita L.) grown under tropical condition. Advances in Agricultural Science, 6:123-131.
• Donato LTF, Donato SLR, Brito CFB, Fonseca VA, Gomes CN & Rodrigues Filho VA (2020) Estimating leaf area of prata-type banana plants with lanceolate type leaves. Revista Brasileira de Fruticultura, 42:01-07.
• Goergen PCH, Lago I, Schwab NT, Alves AF, Freitas CPO & Selli VS (2021) Allometric relationship and leaf area modeling estimation on chia by non-destructive method. Revista Brasileira de Engenharia Agrícola e Ambiental, 25:305-311.
• Grin-Global (2016) Taxon: Ocimum gratissimum L. Available at: <https://npgsweb.arsgrin.gov/gringlobal/taxonomydetail.aspx?id=25483>. Accessed on: April 23^th, 2022.
  » https://npgsweb.arsgrin.gov/gringlobal/taxonomydetail.aspx?id=25483>
• Hzounda JBF, Jazet PMD, Lazar G, Raducanu D, Caraman I, Bassene E, Boyom FF & Lazar IM (2016) Spectral and chemometric analyses reveal antioxidant properties ofessential oils from four Cameroonian Ocimum Industrial Crops and Products, 80:101-108.
• Janssen PHM & Heuberger PSC (1995) Calibration of process-oriented models. Ecological Modelling, 83:55-66.
• Leite MLMV, Lucena LRR, Sá Júnior EH & Cruz MG (2017) Estimativa da área foliar em Urochloa mosambicensis por dimensões lineares. Revista Agropecuária Técnica, 38:9-16.
• Lucena LRR, Leite MLMV, Simões VJLP, Nóbrega C, Almeida MCR & Simplício JB (2021) Estimating the area and weight of cactus forage cladodes using linear dimensions. Acta Scientiarum. Agronomy, 43:01-10.
• Macário APS, Ferraz RLS, Costa PS, Brito Neto JF, Melo AS & Dantas Neto J (2020) Allometric models of estimating Moringa oleífera leaflets area. Ciência e Agrotecnologia, 44:1-10.
• Marshall JK (1968) Methods of leaf area measurement of large and small leaf samples. Photosynthetica, 2:41-47.
• Monga S, Dhanwal P, Kumar R, Kumar A & Chhokar V (2017) Pharmacological and physico-chemical properties of Tulsi (Ocimum gratissimum L.): An updated review. Pharmaceutical Innovation, 6:181-186.
• Monteiro PC, Majolo C, Chaves FCM, Bizzo HR, O’Sullivan FLA & Chagas EC (2021) Antimicrobial activity of essential oils from Lippia sidoides, Ocimum gratissimum and Zingiber officinale against Aeromonas spp.. Journal of Essential Oil Research, 33:152-161.
• Mota CS, Leite HG & Cano MAO (2014) Equações para estimar área foliar de folíolos de Acrocomia aculeta Pesquisa Florestal Brasileira, 34:217-224.
• Oyelakin AO, Olubode TP & Olawale BR (2020) Ethnobotanical survey and phytochemical analysis of selected medicinal plants used in treating digestive disorder. Journal of Medicinal Plants Studies, 8:38-42.
• Peksen E (2007) Non-destructive leaf area estimation model for faba bean (Vicia faba L.). Scientia Horticulturae, 113:322-328.
• R Development Core Team (2020) R: A language and environment for statistical computing. Available at: <https://www.r-project.org/>. Accessed on: April 15^th, 2022.
  » https://www.r-project.org/
• Ribeiro JES, Barbosa AJS, Lopes SF, Pereira WE & Albuquerque MB (2018a) Seasonal variation in gas exchange by plants of Erythroxylum simonis Plowman. Acta Botanica Brasilica, 32:287-296.
• Ribeiro JES, Barbosa AJS & Albuquerque MB (2018b) Leaf area estimate of Erythroxylum simonis Plowman by linear dimensions. Floresta e Ambiente, 25:01-07.
• Ribeiro JES, Coêlho ES, Figueiredo FRA, Lopes SF & Albuquerque MB (2019a) Estimation of leaf area of Erythroxylum citrifolium from linear leaf dimensions. Bioscience Journal, 35:1923-1931.
• Ribeiro JES, Coêlho ES, Figueiredo FRA, Pereira WE & Albuquerque MB (2019b) Leaf area estimation for Psychotria carthagenensis and Psychotria hoffmannseggiana as a function of linear leaf dimensions. Acta Scientiarum. Biological Sciences, 41:01-08.
• Ribeiro JES, Nóbrega JS, Figueiredo FRA, Ferreira JTA, Pereira WE, Bruno RLA & Albuquerque MB (2020a) Estimativa da área foliar de Mesosphaerum suaveolens a partir de relações alométricas. Rodriguésia, 71:01-09.
• Ribeiro JES, Coêlho ES, Figueiredo FRA & Melo MF (2020b) Non-destructive method for estimating leaf area of Erythroxylum pauferrense (Erythroxylaceae) from linear dimensions of leaf blades. Acta Botanica Mexicana, 127:01-12.
• Ribeiro JES, Figueiredo FRA, Coêlho ES & Melo MF (2021) Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg. Bioscience Journal, 37:01-09.
• Ribeiro JES, Coêlho ES, Almeida PHA, Lopes WAR, Silva EF, Oliveira AKS, Silveira LM, Silva DV, Barros Júnior AP & Dias TJ (2022a) Allometric models to estimate peanuts leaflets area by non-destructive method. Bragantia, 81:01-13.
• Ribeiro JES, Nobrega JS, Coelho ES, Dias TJ & Melo MF (2022b) Estimating leaf area of basil cultivars through linear dimensions of leaves. Ceres, 69:139-147.
• Ribeiro JES, Figueiredo FRA, Nóbrega JS, Coêlho ES & Melo MF (2022c) Leaf area of Erythrina velutina Willd. (Fabaceae) through allometric equations. Floresta, 52:93-102.
• Ribeiro JES, Coêlho ESC, Oliveira AKS, Silva AGC, Lopes WAR, Oliveira PHA, Silva EF, Barros Júnior & Silveira LM (2023a) Artificial neural network approach for predicting the sesame (Sesamum indicum L.) leaf area: A non-destructive and accurate method. Heliyon, 9:1-12.
• Ribeiro JES, Coelho ES, Pessoa AMS, Oliveira AKS, Oliveira AMF, Barros Júnior AP, Mendonca V & Nunes GHS (2023b) Nondestructive method for estimating the leaf area of sapodilla from linear leaf dimensions. Revista Brasileira de Engenharia Agrícola e Ambiental, 27:209-215.
• Santana BF, Voeks RA & Funch LS (2016) Ethnomedicinal survey of a maroon community in Brazil’s Atlantic tropical forest. Journal of Ethnopharmacology, 181:37-49.
• Santos JNB, Jarma-Orozco A, Antunes WC, Mendes KR, Figueiroa JM, Pessoa JM & Pompelli MF (2021) New approaches to predict leaf area in woody tree species from the Atlantic Rainforest, Brazil. Austral Ecology, 46:01-14.
• Silva SF, Pereira LR, Cabanez PA, Mendonça RF & Amaral JAT (2017) Modelos alométricos para estimativa da área foliar de boldo pelo método não destrutivo. Agrarian, 10:193-198.
• Silva TI, Ribeiro JES, Dias MG, Cruz RRP, Macêdo LF, Nóbrega JS, Sales GNB, Santos EP, Costa FB & Grossi JAS (2023) Non-destructive method for estimating chrysanthemum leaf area. Revista Brasileira de Engenharia Agrícola e Ambiental, 27:934-940.
• Sousa LF, Santos JGD, Alexandrino E, Maurício RM, Martins AD & Sousa JTL (2015) Método prático e eficiente para estimar a área foliar de gramíneas forrageiras tropicais. Archivos de Zootecnia, 64:83-85.
• Taiz L, Zeiger E, Møller IM & Murphy A (2017) Fisiologia e desenvolvimento vegetal. Artmed. Porto Alegre, Brasil. 888p.
• Teobaldelli M, Basile B, Giuffrida F, Romano D, Toscano S, Leonardi C, Rivera CM, Colla G & Rouphael Y (2020) Analysis of cultivar-specific variability in size-related leaf traits and modeling of single leaf area in three medicinal and aromatic plants: Ocimum basilicum L., Mentha Spp., and Salvia Spp. Plants, 9:1-21.
• Toebe M, Soldateli FJ, Souza RR, Mello AC & Segatto A (2021) Leaf area estimation of Burley tobacco. Ciência Rural, 51:01-09.
• Tondjo K, Brancheriau L, Sabatier SA, Kokutse AD, Akossou A, Kokou K & Fourcaud T (2015) Non-destructive measurement of leaf area and dry biomass in Tectona grandis Trees, 29:1625-1631.
• Willmott CJ (1981) On the validation of models. Physical Geography, 2:184-194.

Publication Dates

• Publication in this collection
  20 Oct 2023
• Date of issue
  2023

History

• Received
  10 May 2022
• Accepted
  20 Mar 2023