Uso de visão computacional na classificação de capim Xaraés segundo o status nutricional em nitrogênio

Mancin, Wellington Renato; Pereira, Lilian Elgalise Techio; Carvalho, Rachel Santos Bueno; Shi, Yeyin; Silupu, Wilson Manuel Castro; Tech, Adriano Rogério Bruno

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Revista Ciência Agronômica

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Scientific Article • Rev. Ciênc. Agron. 53 • 2022 • https://doi.org/10.5935/1806-6690.20220006 copiar

Uso de visão computacional na classificação de capim Xaraés segundo o status nutricional em nitrogênio

Autoria SCIMAGO INSTITUTIONS RANKINGS

RESUMO

Este estudo baseia-se no princípio de que índices de vegetação (IVs), derivados do modelo de cores RGB obtidos de imagens digitais, podem ser usados para caracterizar assinaturas espectrais e classificar pastos de Brachiaria brizantha cv. Xaraés segundo o status de nitrogênio (N). A partir de dados colorimétricos obtidos de imagens de lâminas foliares adquiridas em campo, três redes neurais artificiais foram avaliadas quanto ao desempenho na classificação do status N: Feedforward Backpropagation (FFBP), Cascade Forward Backpropagation (CFBP) e função de Base Radial (RBFNN). Quatro taxa s de fertilização nitrogenada foram aplicadas para gerar contrastes no teor de N nos tecidos das plantas. As folhas mais jovens completamente expandidas de 60 perfilhos foram destacadas das plantas a cada ciclo de rebrotação de 28 dias, sendo as imagens das folhas e teor de N foliar determinados. As amostras foram classificadas em deficientes (> 17 g N kg^-1 de matéria seca (MS)), moderadamente deficientes (de 17,1 a 20,0 g N kg^-1 MS) e suficiente (> 20,1 g N kg^-1 MS). Os IVs foram selecionados pela análise de componentes principais e o desempenho das redes avaliados pela acurácia. As acurácias nas classificações obtidas pelas redes foram de 88%, 86% e 79% para FFBP, CFBP e RBFNN, respectivamente, indicando que as assinaturas espectrais podem ser determinadas a partir de imagens adquiridas em campo. Assim, o método proposto pode ser utilizado para desenvolver um software de monitoramento do status de N em tempo real, fornecendo uma ferramenta rápida e econômica para definir o momento e quantidade de fertilizantes nitrogenados, conforme demanda da pastagem.

Palavras-chave:
Processamento de imagens; Sensoriamento remoto; HSB; Assinatura espectral

¹ 1 GN, RN and BN represent normalized Green, Red and Blue, respectively; H, S and Br represent hue, saturation and brightness, respectively; VARIgreen represents the Visible Atmospherically Resistant Index; DGCI represents the Dark Green Color Index; ExG, ExR and ExGR represent excess of green, excess of red and excess of green minus excess of red; MExG is the modified excess of green; CIVE represents Colour Index of Vegetation Extraction; VEG represents the Vegetative Index and COM a Combined Index VIs	Formulae	References
G_N	$G_{N} = G / (R + G + B)$	*Yang et al.* (2015)**YANG, W. et al. Greenness identification based on HSV decision tree. Information Processing in Agriculture, v. 2, n. 3/4, p. 149-160, 2015.
R_N	$R_{N} = R / (R + G + B)$	*Yang et al.* (2015)**YANG, W. et al. Greenness identification based on HSV decision tree. Information Processing in Agriculture, v. 2, n. 3/4, p. 149-160, 2015.
B_N	$B_{N} = B / (R + G + B)$	*Yang et al.* (2015)**YANG, W. et al. Greenness identification based on HSV decision tree. Information Processing in Agriculture, v. 2, n. 3/4, p. 149-160, 2015.
H	$H = 60 * {(G - B) / [\max (R, G, B) - \min (R, G, B)]}$ , if max(R,G,B)=R; or $60 * {2 + {(B - R) / [\max (R, G, B) - \min (R, G, B)]}}$ , if max(R,G,B)=G; or $60 * {4 + {(R - G) / [\max (R, G, B) - \min (R, G, B)]}}$ , if max(R,G,B)=B	*Wang et al.* (2014)**WANG, Y. et al. Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light. Plant Methods, v. 10, n. 1, p. 36, 2014.
S	$S = (\max (R, G, B) - \min (R, G, B)) / \max (R, G, B)$	*Wang et al.* (2014)**WANG, Y. et al. Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light. Plant Methods, v. 10, n. 1, p. 36, 2014.
Br	$Br = \max (R, G, B) / 225$	*Wang et al.* (2014)**WANG, Y. et al. Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light. Plant Methods, v. 10, n. 1, p. 36, 2014.
VARIgreen	$VARIgreen = (G - R) / (G + R - B)$	*Gitelson et al.* (2002)**GITELSON, A. A. et al. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, v. 80, n. 1, p. 76-87, 2002.
DGCI	$DGCI = [(Hue - 60) / 60 + (1 - Saturation) + (1 - Brightness)] / 3$	Karcher and Richardson (2003)KARCHER, D. E.; RICHARDSON, M. D. Quantifying turfgrass color using digital image analysis. Crop Science, v. 43, n. 3, p. 943-951, 2003.
ExG	$ExG = (2 * G_{N}) - R_{N} - B_{N}$	*Guijarro et al.* (2011)GUIJARRO, M. et al. Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture, v. 75, n. 1, p. 75-83, 2011. and Yang et al. (2015)**YANG, W. et al. Greenness identification based on HSV decision tree. Information Processing in Agriculture, v. 2, n. 3/4, p. 149-160, 2015.
ExR	$ExR = (1.4 * R_{N}) - G_{N}$	*Guijarro et al.* (2011)**GUIJARRO, M. et al. Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture, v. 75, n. 1, p. 75-83, 2011.
ExGR	$ExGR = ExG - ExR$	*Guijarro et al.* (2011)GUIJARRO, M. et al. Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture, v. 75, n. 1, p. 75-83, 2011. and Yang et al. (2015)**YANG, W. et al. Greenness identification based on HSV decision tree. Information Processing in Agriculture, v. 2, n. 3/4, p. 149-160, 2015.
MExG	$MExG = 1.262 * G - 0.884 * R - 0.311 * B$	*Burgos-Artizzu et al.* (2011)**BURGOS-ARTIZZU, X. P. et al. Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, v. 75, n. 2, p. 337-346, 2011.
CIVE	$CIVE = 0.441 R_{N} - 0.811 G_{N} + 0.385 B_{N} + 18.78745$	*Guijarro et al.* (2011)GUIJARRO, M. et al. Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture, v. 75, n. 1, p. 75-83, 2011. and Yang et al. (2015)**YANG, W. et al. Greenness identification based on HSV decision tree. Information Processing in Agriculture, v. 2, n. 3/4, p. 149-160, 2015.
VEG	$VEG = G_{N} / (R_{N}^{0.667}) * (B_{N}^{0.333})$	*Guijarro et al.* (2011)GUIJARRO, M. et al. Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture, v. 75, n. 1, p. 75-83, 2011. and Yang et al. (2015)**YANG, W. et al. Greenness identification based on HSV decision tree. Information Processing in Agriculture, v. 2, n. 3/4, p. 149-160, 2015.
COM	$COM = 0.25 ExG + 0.30 ExGR + 0.33 CIVE + 0.12 VEG$	*Yang et al.* (2015)**YANG, W. et al. Greenness identification based on HSV decision tree. Information Processing in Agriculture, v. 2, n. 3/4, p. 149-160, 2015.

VIs of images	Sufficient plants	Moderately deficient plants	Deficient plants	p-value Quadratic model	R² Quadratic model
R	140.9 ± 0.83	143.9 ± 0.85	151.1 ± 1.03	< 0.0001	0.26
G	164.4 ± 0.78	167.2 ± 0.90	176.4 ± 1.04	< 0.0001	0.31
B	118.4 ± 1.44 0.33 ± 0.0008	118.1 ± 1.22	109.1 ± 1.06 0.34 ± 0.0007	< 0.0001	0.12
R_N	0.33 ± 0.0008	0.33 ± 0.0005	0.34 ± 0.0007	<0.0001	0.47
G_N	0.39 ± 0.001	0.39 ± 0.001	0.40 ± 0.0009	< 0.0001	0.26
B_N	0.28 ± 0.002 91.9 ± 0.62	0.27 ± 0.002	0.25 ± 0.001 82.9 ± 0.39	< 0.0001	0.39
H	91.9 ± 0.62	88.8 ± 0.46	82.9 ± 0.39	< 0.0001	0.47
S	0.28 ± 0.008	0.29 ± 0.007	0.38 ± 0.005	< 0.0001	0.34
Br	0.64 ± 0.003 0.16 ± 0.005	0.66 ± 0.004	0.69 ± 0.004 0.21 ± 0.003	< 0.0001	0.31
¹ 1 Only the vegetation indices identified in the principal component analysis are shown. Leaves were considered deficient when showing a N content below 17 g N kg-1 DW; moderately deficient when showing a range from 17.1 to 20.0 g N kg-1 DW, and sufficient when showing a leaf N content above 20.1 g N kg-1 DW ExG	0.16 ± 0.005	0.17 ± 0.004	0.21 ± 0.003	< 0.0001	0.24
ExR	0.077 ± 0.001	0.079 ± 0.001	0.080 ± 0.001	= 0.0293	0.031
CIVE	18.7 ± 0.002	18.7 ± 0.002	18.7 ± 0.001	< 0.0001	0.23
VEG	1.24 ± 0.007	1.24 ± 0.007	1.30 ± 0.005	< 0.0001	0.20
COM VARI	6.39 ± 0.003	6.39 ± 0.003	6.41 ± 0.002	< 0.0001	0.18
VARI	0.13 ± 0.002	0.12 ± 0.003	0.12 ± 0.002	= 0.0006	0.06

	PRIN1	PRIN2
Eigenvalues	11.70	4.25
Proportion	0.650	0.236
Accumulated	0.650	0.886
Criterion for cutting	0.28	0.28
¹ 1 R, G, GN and BN represent Red, Green, normalized Green and normalized Blue, respectively; S and Br represent saturation and brightness, respectively; ExG and ExR represent excess of green and excess of red, respectively; CIVE represents Colour Index of Vegetation Extraction; VEG represents the Vegetative Index; COM is a Combined Index and VARIgreen represents the Visible Atmospherically Resistant Index VIs	Eigenvectors
R	0.045	0.440
G	0.130	0.319
G_N	0.289	-0.071
B_N	-0.284	-0.070
S	0.290	0.022
Br	0.130	0.319
ExG	0.289	-0.071
ExR	-0.120	0.391
CIVE	-0.288	0.080
VEG	0.284	-0.109
COM	0.282	-0.125
VARIgreen	0.086	-0.405

Network	Topology	AF	TF	LR	Performance
Network	Topology	AF	TF	LR	R² Train.	R² Valid	R² Test	R² General	³Accuracy (%)
FFBP	13-5-1	Tansig	¹LM	0.1	0.85	0.81	0.79	0.83	88
CFBP	13-5-1	Tansig	LM	0.1	0.79	0.94	0.84	0.81	86
FFBP	13-10-1	Tansig	LM	0.1	0.78	0.82	0.77	0.79	80
CFBP	13-10-1	Tansig	LM	0.1	0.79	0.81	0.82	0.80	82
FFBP	13-15-1	Tansig	LM	0.1	0.77	0.76	0.86	0.79	80
CFBP	13-15-1	Tansig	LM	0.1	0.81	0.83	0.81	0.81	84
²RBFNN	13-148-1	Radial Base	²RBF	-	0.78	0.77	0.78	0.77	79

	Regrowth cycle
Classes of N status¹ 1 Leaves were considered deficient when showing a leaf N below 17 g N kg-1 DW; moderately deficient when showing a range from 17.1 to 20.0 g N kg-1 DW, and sufficient when showing a leaf N above 20.1 g N kg-1 DW.	Period 1 (December)	Period 2 (January)	Period 3 (February)	Period 4 (March)
(D) Deficient	15.0 ± 0.25	15.2 ± 0.34	15.8 ± 0.38	16.9 ± 0.48
Estimated N rate to attain sufficiency² 2 Estimated N rate (ENR) calculated from equation ENR=[(1/0.0081)*ln(20/N content of the class)]	35	35	30	20
(MD) Moderately deficient	18.6 ± 0.38	18.2 ± 0.49	18.5 ± 0.27	18.6 ± 0.41
Estimated N rate to attain sufficiency	30	30	20	15
(S) Sufficient	19.8 ± 0.68	22.7 ± 0.27	22.8 ± 0.39	23.6 ± 0.27
Estimated N rate to attain sufficiency	0	0	0	0

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[1] Editor-in-Article: Profa. Andrea Pereira Pinto - deiapp@hotmail.com