Leaf area estimation of Burley tobacco

Toebe, Marcos; Soldateli, Francis Junior; Souza, Rafael Rodrigues de; Mello, Anderson Chuquel; Segatto, Alexandre

doi:10.1590/0103-8478cr20200071

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CROP PRODUCTION • Cienc. Rural 51 (1) • 2021 • https://doi.org/10.1590/0103-8478cr20200071 copy

Leaf area estimation of Burley tobacco

Estimativa da área foliar de tabaco do tipo Burley

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT:

Leaf area is an important growth variable in agricultural crops and the leaf is the main variable of interest in the tobacco industry. So, the aim of this scientific research was to estimate the Burley tobacco leaf area by linear dimensions of the leaves and to determine which mathematical model is more adequate for this purpose. Two experiments were carried out with Burley tobacco, cultivar DBH 2252, in 2016/2017 and 2018/2019 agricultural years, respectively, in the municipalities of Itaqui and Vanini - RS - Brazil. In 600 leaves were measured length (L), width (W), length×width product (LW), length/width ratio (L/W) and determined the real leaf area (LA). Four hundred and fifty leaves were separated to generate models of the leaf area as a function of linear dimension and the other 150 leaves were used for model’s validation. The power model LA = 0.5037LW^1.04435 (R² = 0.9960) is the most adequate for Burley tobacco ‘DBH 2252’ leaf area estimation. Alternatively, the models LA=2.0369W^1.8619 (R²=0.9796) and LA=0.1222L^2.2771 (R²=0.9738) based on width and length, respectively, can be used when only one leaf dimension is measured.

Key words:
Nicotiana tabacum L.; image processing; mathematical models.

Authorship

Marcos Toebe ^*E-mail: m.toebe@gmail.com. *Corresponding author

Departamento de Ciências Agronômicas e Ambientais, Universidade Federal de Santa Maria (UFSM), 98400-000, Frederico Westphalen, RS, Brasil. Universidade Federal de Santa Maria (UFSM)BrazilFrederico Westphalen, RS, BrazilDepartamento de Ciências Agronômicas e Ambientais, Universidade Federal de Santa Maria (UFSM), 98400-000, Frederico Westphalen, RS, Brasil.

https://orcid.org/0000-0003-2033-1467

Francis Junior Soldateli

Programa de Pós-graduação em Agronomia, Universidade Federal de Santa Maria (UFSM), Santa Maria, RS, Brasil. Universidade Federal de Santa Maria (UFSM)BrazilSanta Maria, RS, BrazilPrograma de Pós-graduação em Agronomia, Universidade Federal de Santa Maria (UFSM), Santa Maria, RS, Brasil.

https://orcid.org/0000-0002-0721-1519

Rafael Rodrigues de Souza

Programa de Pós-graduação em Agronomia, Agricultura e Ambiente, Universidade Federal de Santa Maria (UFSM), Frederico Westphalen, RS, Brasil. Universidade Federal de Santa Maria (UFSM)BrazilFrederico Westphalen, RS, BrazilPrograma de Pós-graduação em Agronomia, Agricultura e Ambiente, Universidade Federal de Santa Maria (UFSM), Frederico Westphalen, RS, Brasil.

https://orcid.org/0000-0002-7068-4079

Anderson Chuquel Mello

https://orcid.org/0000-0003-4509-957X

Alexandre Segatto

Engenheiro Agrônomo, NUFARM BRASIL®, Santo Ângelo, RS, Brasil.NUFARM BRASIL®BrasilSanto Ângelo, RS, BrasilEngenheiro Agrônomo, NUFARM BRASIL®, Santo Ângelo, RS, Brasil.

https://orcid.org/0000-0003-3586-0409

E-mail: m.toebe@gmail.com. ^*Corresponding author

DECLARATION OF CONFLICT OF INTERESTS

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

AUTHORS’ CONTRIBUTIONS

All authors conceived the experiments. FJS, RRS, ACM and AS performed the experiments and data collection. MT supervised and coordinated the experiments. All authors performed the image processing. MT performed the statistical analyses. MT and RRS prepared the draft of the manuscript. All authors critically revised the manuscript and approved of the final version.

SCIMAGO INSTITUTIONS RANKINGS

Engenheiro Agrônomo, NUFARM BRASIL®, Santo Ângelo, RS, Brasil.NUFARM BRASIL®BrasilSanto Ângelo, RS, BrasilEngenheiro Agrônomo, NUFARM BRASIL®, Santo Ângelo, RS, Brasil.

Figures | Tables

Figures (3)
Tables (2)

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Figure 1
Linear measurements (length and width) of Burley tobacco (Nicotiana tabacum L.) cultivar DBH 2252.

Thumbnail

Figure 2
Frequency histogram (diagonally) and scatter plots for variables in 450 leaves used for generation and 150 leaves used for validation of the leaf area estimation models of Burley tobacco (Nicotiana tabacum L.) cultivar DBH 2252.

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Figure 3
Linear, quadratic and power models for the real leaf area (LA) estimation as a function of linear dimension: a) Length; b) Width; c) Length×Width product, generated based on n = 450 leaves and coefficient of determination (R²) in each model for Burley tobacco (Nicotiana tabacum L.) cultivar DBH 2252.

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Table 1
Descriptive statistics for variables of leaves used in the model’s generation and validation of leaf area estimation in Burley tobacco (Nicotiana tabacum L.), cultivar DBH 2252.

Thumbnail

Table 2
Models validation based on indicators: linear coefficient (a), slope coefficient (b), Pearson correlation coefficient (r) and determination coefficient (R²), mean absolute error (MAE), root mean square error (RMSE) and d Willmott index (d), calculated between observed and estimated leaf area of 150 leaves of Burley tobacco (Nicotiana tabacum L.), cultivar DBH 2252.

Figure 1 Linear measurements (length and width) of Burley tobacco (Nicotiana tabacum L.) cultivar DBH 2252.

Figure 2 Frequency histogram (diagonally) and scatter plots for variables in 450 leaves used for generation and 150 leaves used for validation of the leaf area estimation models of Burley tobacco (Nicotiana tabacum L.) cultivar DBH 2252.

Figure 3 Linear, quadratic and power models for the real leaf area (LA) estimation as a function of linear dimension: a) Length; b) Width; c) Length×Width product, generated based on n = 450 leaves and coefficient of determination (R²) in each model for Burley tobacco (Nicotiana tabacum L.) cultivar DBH 2252.

Table 1 Descriptive statistics for variables of leaves used in the model’s generation and validation of leaf area estimation in Burley tobacco (Nicotiana tabacum L.), cultivar DBH 2252.

Variable^(a)	-----------------------------------------------------------------Descriptive statistics^(b)---------------------------------------------------------
	Min	Max	Mean	SD	CV	SE	Asym^(c)	Kurt^(d)
---------------------------------Data collected at 67 days after transplantation in the first experiment (n = 100 leaves)------------------------------
L (cm)	7.30	51.70	32.00	13.01	40.66	1.30	-0.40 ^ns	-1.19^*
W (cm)	2.80	36.40	18.20	9.51	52.27	0.95	-0.09 ^ns	-1.18^*
LW (cm²)	20.44	1806.42	701.67	515.26	73.43	51.53	0.26 ^ns	-1.09^*
L/W	1.30	2.88	1.95	0.40	20.61	0.04	0.61^*	-0.62 ^ns
LA (cm²)	11.20	1320.48	475.57	364.48	76.64	36.45	0.37 ^ns	-0.95^*
-------------------------------Data collected at 76 days after transplantation in the first experiment (n = 100 leaves)--------------------------------
L (cm)	9.90	58.50	36.95	13.46	36.42	1.35	-0.47^*	-0.84 ^ns
W (cm)	3.50	40.70	20.12	9.69	48.17	0.97	-0.14 ^ns	-1.08^*
LW (cm²)	35.64	2356.53	869.03	596.31	68.62	59.63	0.30 ^ns	-0.84 ^ns
L/W	1.42	2.92	2.02	0.41	20.47	0.04	0.62^*	-0.68 ^ns
LA (cm²)	20.76	1622.51	602.31	428.64	71.17	42.86	0.34 ^ns	-0.88 ^ns
-------------------------------Data collected at 87 days after transplantation in the first experiment (n = 100 leaves)--------------------------------
L (cm)	14.70	62.10	42.36	11.00	25.96	1.10	-0.65^*	-0.37 ^ns
W (cm)	6.30	36.90	22.10	6.87	31.10	0.69	-0.48^*	-0.58 ^ns
LW (cm²)	92.61	2291.49	1010.18	497.65	49.26	49.77	-0.02 ^ns	-0.67 ^ns
L/W	1.68	2.34	1.96	0.16	8.15	0.02	0.73^*	-0.40 ^ns
LA (cm²)	54.92	1647.06	692.03	356.61	51.53	35.66	0.03 ^ns	-0.68 ^ns
------------------------------Data collected at 95 days after transplantation in the first experiment (n = 100 leaves)---------------------------------
L (cm)	8.90	59.90	39.58	14.71	37.18	1.47	-0.49^*	-1.14^*
W (cm)	3.10	38.30	19.60	9.56	48.78	0.96	0.06 ^ns	-1.22^*
LW (cm²)	27.59	2198.42	909.38	637.99	70.16	63.80	0.24 ^ns	-1.20^*
L/W	1.50	2.87	2.17	0.34	15.75	0.03	-0.08 ^ns	-0.96^*
LA (cm²)	18.84	1641.23	637.63	466.10	73.10	46.61	0.37 ^ns	-1.02^*
------------------------------Data collected at 104 days after transplantation in the first experiment (n = 100 leaves)-------------------------------
L (cm)	12.00	69.90	40.76	14.40	35.33	1.44	0.04 ^ns	-0.70 ^ns
W (cm)	6.50	41.70	21.62	8.37	38.74	0.84	0.35 ^ns	-0.61 ^ns
LW (cm²)	85.20	2698.14	995.29	673.23	67.64	67.32	0.78^*	-0.20 ^ns
L/W	1.54	2.32	1.91	0.18	9.56	0.02	0.02 ^ns	-0.66 ^ns
LA (cm²)	43.99	2258.41	697.63	509.07	72.97	50.91	1.03^*	0.59 ^ns
------------------------------Data collected at 110 days after transplantation in the second experiment (n = 100 leaves)----------------------------
L (cm)	11.30	85.20	50.25	14.45	28.77	1.45	-0.19 ^ns	0.22 ^ns
W (cm)	4.60	47.60	23.15	8.17	35.30	0.82	0.40 ^ns	0.70 ^ns
LW (cm²)	51.98	4055.52	1272.49	764.78	60.10	76.48	1.14^*	1.58^*
L/W	1.62	2.98	2.24	0.31	13.99	0.03	0.41 ^ns	-0.52 ^ns
LA (cm²)	37.14	2893.62	884.22	531.99	60.16	53.20	1.23^*	2.04^*
-----------------------------------------------------Data used in the model’s generation (n = 450 leaves)-------------------------------------------------
L (cm)	7.30	85.20	41.15	14.35	34.87	0.68	-0.25^*	-0.21^ns
W (cm)	2.80	47.60	21.19	8.74	41.24	0.41	-0.11^ns	-0.53^*
LW (cm²)	20.44	4055.52	987.74	636.17	64.41	29.99	0.73^*	1.20^*
L/W	1.30	2.98	2.04	0.35	17.00	0.02	0.52^*	-0.26^ns
LA (cm²)	11.20	2893.62	683.03	455.19	66.64	21.46	0.79^*	1.33^*
--------------------------------------------------Data used in the model’s validation (n = 150 leaves)----------------------------------------------------
L (cm)	9.20	72.30	37.82	15.11	39.95	1.23	-0.03^ns	-0.67^ns
W (cm)	3.60	41.70	19.61	9.20	46.93	0.75	0.07^ns	-0.77^*
LW (cm²)	34.04	2834.16	875.47	654.58	74.77	53.45	0.80^*	0.20^ns
L/W	1.47	2.84	2.04	0.31	15.37	0.03	0.68^*	-0.11^ns
LA (cm²)	18.26	2258.41	610.50	481.40	78.85	39.31	0.99^*	0.76^ns

^(a) L = length; W = width; LW = length×width; L/W = length / width ratio; LA = real leaf area. ^(b) Min = minimum; Max = maximum; Mean = mean; SD = standard deviation; CV = coefficient of variation; SE = standard error; Asym = asymmetry; Kurt = kurtosis. ^(c)*Asymmetry differs from zero by the t-test at 5% probability level. ^ns non-significant. ^(d)*Kurtosis differs from zero by t-test at 5% probability level. ^ns non-significant.

Table 2 Models validation based on indicators: linear coefficient (a), slope coefficient (b), Pearson correlation coefficient (r) and determination coefficient (R²), mean absolute error (MAE), root mean square error (RMSE) and d Willmott index (d), calculated between observed and estimated leaf area of 150 leaves of Burley tobacco (Nicotiana tabacum L.), cultivar DBH 2252.

Model Type	Independent variable	a^(a)	b^(b)	r^(c)	R²	MAE	RMSE	d
1) Linear	Length	356.724 ^*	0.531 ^*	0.94 ^*	0.884	204.462	254.006	0.832
2) Quadratic	Length	64.432 ^*	0.870 ^*	0.969 ^*	0.940	73.195	123.787	0.951
3) Power	Length	23.466 ^ns	0.923 ^*	0.968 ^*	0.938	71.113	122.084	0.952
4) Linear	Width	292.653 ^*	0.638 ^*	0.959 ^*	0.921	173.844	208.993	0.894
5) Quadratic	Width	53.821^*	0.921 ^*	0.988 ^*	0.977	53.469	77.655	0.982
6) Power	Width	26.093 ^*	0.958 ^*	0.988 ^*	0.977	47.975	72.976	0.983
7) Linear	Length×Width	36.186 ^*	0.941 ^*	0.992 ^*	0.984	33.489	63.783	0.988
8) Quadratic	Length×Width	26.542 ^*	0.950 ^*	0.992 ^*	0.985	31.421	61.131	0.988
9) Power	Length×Width	15.889 ^*	0.962 ^*	0.992 ^*	0.985	30.605	59.848	0.989

^(a)*Linear coefficient differs from zero, according to the t test at P<0.05. ^ns non-significant. ^(b)*Slope coefficient differs from one, according to the t test at p<0.05. ^ns non-significant. ^(c)*Pearson correlation coefficient differs from zero, according to the t test at p<0.05. ^ns non-significant.

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[1] E-mail: m.toebe@gmail.com. ^*Corresponding author