The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen

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

doi:10.5935/1806-6690.20220006

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

The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen¹ 1 Parte da Dissertação do primeiro autor, apresentada ao Programa de Pós-Graduação em Gestão e Inovação na Indústria Animal da Faculdade de Zootecnia e Engenharia de Alimentos (FZEA/USP)

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

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

This study is based on the principle that vegetation indexes (VIs), derived from the RGB color model obtained from digital images, can be used to characterize spectral signatures and classify Brachiaria brizantha cv. Xaraés according to nitrogen status (N). From colorimetric data obtained from leaf blade images acquired in the field, three artificial neural networks were evaluated according to the performance in the classification of N status: Feedforward Backpropagation (FFBP), Cascade Forward Backpropagation (CFBP) and Radial Base function (RBFNN). Four N fertilization rates were applied to generate contrasting N contents in the plants. The youngest completely expanded leaves from 60 tillers were detached at each regrowth cycle of 28 days, thus their images and leaf N content were obtained. Samples were then classified as deficient (>17 g N kg^-1 leaf dry matter (DM), moderately deficient (from 17.1 to 20.0 g N kg^-1 DM), and sufficient (> 20.1 g N kg^-1 DM). The VIs were selected by principal component analysis and the performance of the networks evaluated by the accuracy. The accuracy in classification obtained by the networks were 88%, 86% and 79% for FFBP, CFBP and RBFNN, respectively, indicating that the spectral signatures can be determined from images acquired in the field. So, the proposed method could be used to develop a software that aims to monitor the status of N in real time, providing a fast and inexpensive tool for defining the time and the amount of N fertilizer, according to the pasture demand.

Keywords:
Image processing; Remote sensing; HSB; Spectral signature

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

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The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen1 1 Parte da Dissertação do primeiro autor, apresentada ao Programa de Pós-Graduação em Gestão e Inovação na Indústria Animal da Faculdade de Zootecnia e Engenharia de Alimentos (FZEA/USP)

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

ABSTRACT

The use of computer vision to classify Xaraés grass according to nutritional status in nitrogen¹ 1 Parte da Dissertação do primeiro autor, apresentada ao Programa de Pós-Graduação em Gestão e Inovação na Indústria Animal da Faculdade de Zootecnia e Engenharia de Alimentos (FZEA/USP)