Estimação da área foliar de abobrinha ‘Brasileirinha’ por dimensões foliares

Toebe, Marcos; Souza, Rafael Rodrigues de; Mello, Anderson Chuquel; Melo, Patrícia Jesus de; Segatto, Alexandre; Castanha, Ana Carla

doi:10.1590/0103-8478cr20180932

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CROP PRODUCTION • Cienc. Rural 49 (4) • 2019 • https://doi.org/10.1590/0103-8478cr20180932 linkcopiar

Estimação da área foliar de abobrinha ‘Brasileirinha’ por dimensões foliares

Autoria SCIMAGO INSTITUTIONS RANKINGS

Os objetivos deste trabalho foram estimar a área foliar de abobrinha ‘Brasileirinha’ por dimensões lineares das folhas e testar modelos disponíveis na literatura. Foi conduzido um experimento na safra 2015/16 sendo coletas 500 folhas. Em cada folha foram mensurados comprimento (L), largura (W), calculado produto comprimento×largura (LW) e determinada a área foliar real (LA). Depois, 400 folhas foram separadas para a geração de modelos da área foliar real (LA) em função da dimensão linear (L, W ou LW) de abobrinha. As demais 100 folhas foram utilizadas na validação dos modelos. Um segundo experimento foi conduzido na safra 2016/17, no qual foram coletadas 250 folhas utilizadas na validação dos modelos gerados no primeiro experimento. Existe colinearidade entre L e W e, por isso, os modelos que utilizam o produto LW não são recomendados. O modelo LA=0,5482W²+0,0680W (R²=0,9867) é adequado para a estimação de área foliar de abobrinha ‘Brasileirinha’.

Palavras-chave:
Cucurbita moschata; processamento de imagens; método não destrutivo; modelos matemáticos

Autoria

person Marcos Toebe *

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

0000-0003-2033-1467

person Rafael Rodrigues de Souza

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

0000-0002-7068-4079

person Anderson Chuquel Mello

schoolCurso de Agronomia, Universidade Federal do Pampa (Unipampa), Itaqui, RS, Brasil. Universidade Federal do Pampa (Unipampa),BrazilItaqui, RS, BrazilCurso de Agronomia, Universidade Federal do Pampa (Unipampa), Itaqui, RS, Brasil.

0000-0003-4509-957X

person Patrícia Jesus de Melo

schoolPrograma 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.

0000-0001-6080-4930

person Alexandre Segatto

0000-0003-3586-0409

person Ana Carla Castanha

schoolUPL do Brasil, Tangará da Serra, MT, Brasil.UPL do BrasilBrasilTangará da Serra, MT, BrasilUPL do Brasil, Tangará da Serra, MT, Brasil.

0000-0001-7889-7274

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

DECLARATION OF CONFLICT OF INTEREST

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

SCIMAGO INSTITUTIONS RANKINGS

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

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

Curso de Agronomia, Universidade Federal do Pampa (Unipampa), Itaqui, RS, Brasil. Universidade Federal do Pampa (Unipampa),BrazilItaqui, RS, BrazilCurso de Agronomia, Universidade Federal do Pampa (Unipampa), Itaqui, RS, Brasil.

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.

UPL do Brasil, Tangará da Serra, MT, Brasil.UPL do BrasilBrasilTangará da Serra, MT, BrasilUPL do Brasil, Tangará da Serra, MT, Brasil.

Figuras | Tabelas

Figuras (4)
Tabelas (2)

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Figure 1 Linear measurements (length and width) of a squash ‘Brasileirinha’ leaf.

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Figure 2 Frequency histogram (diagonally) and scatter plots of length, width, length × width and real leaf area of: a) 400 leaves used for generation; b) 100, and; c) 250 leaves used for validation of the models for leaf area estimation of squash ‘Brasileirinha’.

Thumbnail

Figure 3 Models - linear, quadratic and power - of the real leaf area (LA) estimation of squash ‘Brasileirinha’ as a function of linear dimension: a) Length, in cm; b) Width, in cm; c) Length × Width product, in cm², generated based on n=400 leaves, and respective coefficient of determination (R²) for each model.

Thumbnail

Figure 4 Relationship of real leaf area (LA) and the leaf area estimated (LAE) by the model LA=0.5482W²+0.0680W (R²=0.9867) and the residue for each leaf area (LA_i- LAE_i) in: a) 100 leaves (2015/2016 growing season), and; b) 250 leaves (2016/2017 growing season) of squash ‘Brasileirinha’.

Thumbnail

Table 1 Statistics for the variables: length, width, length×width product and real leaf area of leaves used for generation and for validation of the estimation models for leaf area estimation of squash ‘Brasileirinha’.

Thumbnail

Table 2 Validation of models 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 based on observed leaf area and estimated leaf area of leaves from squash ‘Brasileirinha’.

imageFigure 1 Linear measurements (length and width) of a squash ‘Brasileirinha’ leaf. open_in_new

imageFigure 2 Frequency histogram (diagonally) and scatter plots of length, width, length × width and real leaf area of: a) 400 leaves used for generation; b) 100, and; c) 250 leaves used for validation of the models for leaf area estimation of squash ‘Brasileirinha’. open_in_new

imageFigure 3 Models - linear, quadratic and power - of the real leaf area (LA) estimation of squash ‘Brasileirinha’ as a function of linear dimension: a) Length, in cm; b) Width, in cm; c) Length × Width product, in cm², generated based on n=400 leaves, and respective coefficient of determination (R²) for each model. open_in_new

imageFigure 4 Relationship of real leaf area (LA) and the leaf area estimated (LAE) by the model LA=0.5482W²+0.0680W (R²=0.9867) and the residue for each leaf area (LA_i- LAE_i) in: a) 100 leaves (2015/2016 growing season), and; b) 250 leaves (2016/2017 growing season) of squash ‘Brasileirinha’. open_in_new

table_chartTable 1 Statistics for the variables: length, width, length×width product and real leaf area of leaves used for generation and for validation of the estimation models for leaf area estimation of squash ‘Brasileirinha’.

Statistics	Length (L, cm)	Width (W, cm)	Length×Width (LW, cm²)	Real Leaf Area (LA, cm²)
--------------------------------Data used in the generation of models (n=400 leaves) - 2015/2016 growing season----------------------------------
Minimum	2.80	3.40	9.52	7.57
Maximum	16.90	22.80	385.32	296.60
Mean	9.05	12.90	125.05	98.97
Median	9.20	13.30	122.76	96.79
Variance	5.87	12.55	3767.01	2402.75
Standard Deviation	2.42	3.54	61.38	49.02
Coefficient of variation	26.79	27.47	49.08	49.53
Standard Error	0.12	0.18	3.07	2.45
Asymmetry⁽¹⁾	-0.23^ns	-0.35*	0.41*	0.36*
Kurtosis⁽²⁾	-0.20^ns	-0.31^ns	0.28^ns	0.11^ns
P-value of K-S⁽³⁾	>0.20	>0.10	>0.20	>0.20
---------------------------------Data used in the validation of models (n=100 leaves)- 2015/2016 growing season----------------------------------
Minimum	2.40	2.70	6.48	5.79
Maximum	14.80	21.00	310.80	240.99
Mean	9.66	13.78	141.64	111.95
Median	10.05	14.50	144.63	114.05
Variance	6.22	12.52	3950.14	2528.38
Standard Deviation	2.49	3.54	62.85	50.28
Coefficient of variation	25.82	25.68	44.37	44.92
Standard Error	0.25	0.35	6.29	5.03
Asymmetry⁽¹⁾	-0.70^*	-0.78^*	0.07^ns	0.02^ns
Kurtosis⁽²⁾	0.54^ns	0.73^ns	0.03^ns	-0.19^ns
P-value of K-S⁽³⁾	>0.10	>0.15	>0.20	>0.20
--------------------------------Data used in the validation of models (n=250 leaves) - 2016/2017 growing season-----------------------------------
Minimum	6.20	8.20	50.84	38.97
Maximum	14.10	20.10	283.41	226.76
Mean	10.30	14.49	152.98	120.95
Median	10.45	14.70	152.95	121.46
Variance	2.73	5.55	2169.91	1438.59
Standard Deviation	1.65	2.36	46.58	37.93
Coefficient of variation	15.82	16.02	30.46	31.23
Standard Error	0.10	0.15	2.95	2.40
Asymmetry⁽¹⁾	-0.34^*	-0.34^*	-0.08^ns	-0.05^ns
Kurtosis⁽²⁾	-0.71^*	-0.74^*	-0.83^*	-0.88^*
P-value of K-S⁽³⁾	>0.15	>0.05	>0.20	>0.15

^(1)*Asymmetry differs from zero by the t-test at 5% probability level. ^nsnon-significant. ^(2)*Kurtosis differs from zero by t-test at 5% probability level. ^nsnon-significant. ⁽³⁾P-value of the normality test of Kolmogorov-Smirnov.

table_chartTable 2 Validation of models 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 based on observed leaf area and estimated leaf area of leaves from squash ‘Brasileirinha’.

Model Type	Independent variable	a⁽¹⁾	b⁽²⁾	r⁽³⁾	R²	MAE	RMSE	d
--------------------------------------------Validation of models (n = 100 leaves) - 2015/2016 growing season-----------------------------------------
1) Linear	Length	49.850^*	0.549^*	0.960^*	0.922	19.367	24.059	0.906
2) Quadratic	Length	5.029^ns	0.956^ns	0.972^*	0.946	9.431	11.694	0.986
3) Power	Length	0.995^ns	0.994^ns	0.972^*	0.945	9.737	12.049	0.986
4) Linear	Width	49.545^*	0.552^*	0.970^*	0.943	18.744	23.544	0.910
5) Quadratic	Width	2.958^ns	0.972^ns	0.989^*	0.978	5.303	7.425	0.994
6) Power	Width	3.806^*	0.961^*	0.989^*	0.978	5.274	7.475	0.994
7) Linear	Length×Width	2.593^ns	0.978^ns	0.988^*	0.978	5.721	7.504	0.994
8) Quadratic	Length×Width	3.358^ns	0.972^ns	0.988^*	0.978	5.715	7.482	0.994
9) Power	Length×Width	1.439^ns	0.988^ns	0.988^*	0.978	5.709	7.550	0.994
LA = 6.7940 + 0.8259LW (Grecco et al. 2011)	Length×Width	9.499^*	1.020^ns	0.988^*	0.978	12.187	14.222	0.981
LA = 4.77 + 0.61W² (Rouphael et al. 2006)	Width	7.598^*	1.077^*	0.989^*	0.978	16.521	18.600	0.969
-------------------------------------------Validation of models (n = 250 leaves) - 2016/2017 growing season------------------------------------------
1) Linear	Length	59.963^*	0.485^*	0.966^*	0.935	17.091	20.232	0.872
2) Quadratic	Length	9.264^*	0.936^*	0.969^*	0.939	7.470	9.498	0.984
3) Power	Length	4.531^*	0.978^ns	0.968^*	0.939	7.474	9.709	0.984
4) Linear	Width	57.289^*	0.494^*	0.984^*	0.970	16.706	19.848	0.878
5) Quadratic	Width	3.452^*	0.955^*	0.988^*	0.977	4.704	6.092	0.993
6) Power	Width	4.523^*	0.943^*	0.988^*	0.977	4.868	6.314	0.993
7) Linear	Length×Width	4.797^*	0.961^*	0.989^*	0.978	4.358	5.646	0.994
8) Quadratic	Length×Width	5.741^*	0.954^*	0.989^*	0.978	4.393	5.687	0.994
9) Power	Length×Width	3.378^*	0.973^*	0.989^*	0.978	4.336	5.613	0.994
LA = 6.7940 + 0.8259LW (Grecco et al. 2011)	Length×Width	11.797^*	1.003^ns	0.989^*	0.978	12.309	13.468	0.970
LA = 4.77 + 0.61W² (Rouphael et al. 2006)	Width	8.075^*	1.058^*	0.988^*	0.977	15.250	16.569	0.957

^(1)*Linear coefficient differs from zero, according to the t test at P<0.05. ^nsnon-significant. ^(2)*Slope coefficient differs from one, according to the t test at p<0.05. ^nsnon-significant. ^(3)*Pearson correlation coefficient differs from zero, according to the t test at p<0.05. ^ns non-significant.

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[TFN1] ^(1)*Asymmetry differs from zero by the t-test at 5% probability level. ^nsnon-significant. ^(2)*Kurtosis differs from zero by t-test at 5% probability level. ^nsnon-significant. ⁽³⁾P-value of the normality test of Kolmogorov-Smirnov.