Evaluation of the nutritional status of corn by vegetation indices via aerial images

Andrade Junior, Aderson Soares de; Melo, Francisco de Brito; Bastos, Edson Alves; Cardoso, Milton José

doi:10.1590/0103-8478cr20200692

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SOIL SCIENCE • Cienc. Rural 51 (8) • 2021 • https://doi.org/10.1590/0103-8478cr20200692 copy

Evaluation of the nutritional status of corn by vegetation indices via aerial images

Avaliação do estado nutricional do milho por índices de vegetação de imagens aéreas

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT:

The objective of this study is to determine the vegetation indices (IV) as a means of identifying the nutritional status of corn, with respect to the soil nitrogen and potassium, using the aerial images received through an RGB camera loaded on an unmanned aerial vehicle. The images were obtained for an experiment of the nitrogen levels (0, 60, 120 and 180 kg ha^-1) and potassium levels (0, 50, 100 and 150 kg ha^-1), in the random block design, with a factorial scheme of 4 x 4, having three repetitions. Ten leaves were plucked per plot during the flowering phase to assess the total N (NF) and K⁺ leaf contents. The Pearson’s correlation analysis, as well as the analyses of variance and regression between the IV and the concentrations of N and K₂O. NF, K⁺ and the grain yield, responded only to the soil N levels. A significant correlation was observed for the indices of Red Index, Normalized Difference Index and Visible Atmospherically Resistant Index with the NF, which endorses them as favorable in identifying the nutritional standing of corn, with respect to the N level. Not even a single one of the indices evaluated could detect the nutritional ranking of corn in the context of the potassium level.

Key words:
Zea mays L; RGB images; remote sensing; precision agriculture

Universidade Federal de Santa Maria Universidade Federal de Santa Maria, Centro de Ciências Rurais , 97105-900 Santa Maria RS Brazil , Tel.: +55 55 3220-8698 , Fax: +55 55 3220-8695 - Santa Maria - RS - Brazil
E-mail: cienciarural@mail.ufsm.br

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[1] E-mail: aderson.andrade@embrapa.br. ^*Corresponding author

Indices	Sigla	Equation	References
Coloration Index	CI	$= \frac{r - b}{r}$	MANDAL (2016MANDAL, U. K. Spectral color indices-based geospatial modeling of soil organic matter in Chitwan District, Nepal. In: REMOTE SENSING AND SPATIAL INFORMATION SCIENCES, 23., Prague, 2016. Proceedings. Prague: ISPRS, 2016, p.43-48. Available from: <Available from: https://ui.adsabs.harvard.edu/abs/2016ISPAr49B2...43M/abstract >. Accessed: May, 12, 2020. doi: 10.5194/isprs-archives-XLI-B2-43-2016. https://ui.adsabs.harvard.edu/abs/2016IS... )
Color Index of Vegetation Extraction	CIVE	$= 18.78745 + (0.44 r) - (0.88 g) + (0.385 b)$	YANG et al. (2015YANG, W. et al. Greenness identification based on HSV decision tree. Information Processing in Agriculture, v.2, n.3-4, p.149-160, 2015. Available from: <Available from: https://www.sciencedirect.com/science/article/pii/S2214317315000347 >. Accessed: May, 31, 2020. doi: 10.1016/j.inpa.2015.07.003. https://www.sciencedirect.com/science/ar... )
Carotenoid Reflectance Index 1	CRI-1	$= (\frac{1}{b}) - (\frac{1}{g})$	GITELSON et al. (2002GITELSON, A. A. et al. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, v.80, n.1, p.76-87, 2002. Available from: <Available from: https://www.sciencedirect.com/science/article/pii/S0034425701002899 >. Accessed: Feb. 20, 2020. doi: 10.1016/S0034-4257(01)00289-9. https://www.sciencedirect.com/science/ar... )
Carotenoid Reflectance Index 2	CRI-2	$= (\frac{1}{b}) - (\frac{1}{r})$	GITELSON et al. (2002GITELSON, A. A. et al. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, v.80, n.1, p.76-87, 2002. Available from: <Available from: https://www.sciencedirect.com/science/article/pii/S0034425701002899 >. Accessed: Feb. 20, 2020. doi: 10.1016/S0034-4257(01)00289-9. https://www.sciencedirect.com/science/ar... )
Excess Green Index	EXG	$= (2 g) - r - b$	YANG et al. (2015YANG, W. et al. Greenness identification based on HSV decision tree. Information Processing in Agriculture, v.2, n.3-4, p.149-160, 2015. Available from: <Available from: https://www.sciencedirect.com/science/article/pii/S2214317315000347 >. Accessed: May, 31, 2020. doi: 10.1016/j.inpa.2015.07.003. https://www.sciencedirect.com/science/ar... )
Excess Red Index	EXR	$= (1,4 r) - g$	BENDIG et al. (2015BENDIG, J. et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, v.39, p.79-87, 2015. Available from: <Available from: https://www.sciencedirect.com/science/article/abs/pii/S0303243415000446 >. Accessed: May, 12, 2020. doi: 10.1016/j.jag.2015.02.012. https://www.sciencedirect.com/science/ar... )
Excess Green Minus Red Index	EXGR	$= EXG - EXR$	GITELSON et al. (2002GITELSON, A. A. et al. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, v.80, n.1, p.76-87, 2002. Available from: <Available from: https://www.sciencedirect.com/science/article/pii/S0034425701002899 >. Accessed: Feb. 20, 2020. doi: 10.1016/S0034-4257(01)00289-9. https://www.sciencedirect.com/science/ar... )
Green Leaf Index	GLI	$= \frac{(2 g - r - b)}{(2 g + r + b)}$	GITELSON et al. (2002GITELSON, A. A. et al. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, v.80, n.1, p.76-87, 2002. Available from: <Available from: https://www.sciencedirect.com/science/article/pii/S0034425701002899 >. Accessed: Feb. 20, 2020. doi: 10.1016/S0034-4257(01)00289-9. https://www.sciencedirect.com/science/ar... )
Modified Green Red Vegetation Index	MGRVI	$= \frac{(g^{2} - r^{2})}{(g^{2} + r^{2})}$	BENDIG et al. (2015BENDIG, J. et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, v.39, p.79-87, 2015. Available from: <Available from: https://www.sciencedirect.com/science/article/abs/pii/S0303243415000446 >. Accessed: May, 12, 2020. doi: 10.1016/j.jag.2015.02.012. https://www.sciencedirect.com/science/ar... )
Modified Photochemical Reflectance Index	MPRI	$= \frac{(g - r)}{(g + r)}$	BARBOSA et al. (2019BARBOSA, B. D. S. et al. RGB vegetation indices applied to grass monitoring: a qualitative analysis. Agronomy Research, v.17, n.2, p.349-357, 2019. Available from: <Available from: https://agronomy.emu.ee/wp-content/uploads/2019/05/Vol17No2_Barbosa.pdf#abstract-6898 >. Accessed: May, 08, 2020. doi: 10.15159/AR.19.119. https://agronomy.emu.ee/wp-content/uploa... )
Normalized Difference Index	NDI	$= 128 [\frac{(g - r)}{(g + r)} + 1]$	MEYER & CAMARGO NETO (2008MEYER, G. E. et al. Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture, v.63, p.282-293, 2008. Available from: <Available from: https://www.sciencedirect.com/science/article/pii/S0168169908001063 >. Accessed: May, 08, 2020. doi: 10.1016/j.compag.2008.03.009. https://www.sciencedirect.com/science/ar... )
Normalized Green-Blue Difference Index	NGBDI	$= \frac{(g - b)}{(g + b)}$	BENDIG et al. (2015BENDIG, J. et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, v.39, p.79-87, 2015. Available from: <Available from: https://www.sciencedirect.com/science/article/abs/pii/S0303243415000446 >. Accessed: May, 12, 2020. doi: 10.1016/j.jag.2015.02.012. https://www.sciencedirect.com/science/ar... )
Red Green Blue Vegetation Index	RGBVI	$= \frac{(g^{2} - rb)}{(g^{2} + rb)}$	BENDIG et al. (2015BENDIG, J. et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, v.39, p.79-87, 2015. Available from: <Available from: https://www.sciencedirect.com/science/article/abs/pii/S0303243415000446 >. Accessed: May, 12, 2020. doi: 10.1016/j.jag.2015.02.012. https://www.sciencedirect.com/science/ar... )
Red Green Index	RGI	$= \frac{r}{g}$	BENDIG et al. (2015BENDIG, J. et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, v.39, p.79-87, 2015. Available from: <Available from: https://www.sciencedirect.com/science/article/abs/pii/S0303243415000446 >. Accessed: May, 12, 2020. doi: 10.1016/j.jag.2015.02.012. https://www.sciencedirect.com/science/ar... )
Triangular Greenness Index	TGI	$= 0,5 [(r - b) - (r - g)] - [(r - g) - (r - b)]$	BENDIG et al. (2015BENDIG, J. et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, v.39, p.79-87, 2015. Available from: <Available from: https://www.sciencedirect.com/science/article/abs/pii/S0303243415000446 >. Accessed: May, 12, 2020. doi: 10.1016/j.jag.2015.02.012. https://www.sciencedirect.com/science/ar... )
Visible Atmospherically Resistant Index	VARI	$= \frac{(g - r)}{(g + r - b)}$	GITELSON et al. (2002GITELSON, A. A. et al. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, v.80, n.1, p.76-87, 2002. Available from: <Available from: https://www.sciencedirect.com/science/article/pii/S0034425701002899 >. Accessed: Feb. 20, 2020. doi: 10.1016/S0034-4257(01)00289-9. https://www.sciencedirect.com/science/ar... )
Vegetative Index	VEG	$= \frac{g}{(r^{0,667} b^{0,333})}$	HAGUE et al. (2006HAGUE, T. et al. Automated crop and weed monitoring in widely spaced cereals. Precision Agriculture, v.7, p.21-32, 2006. Available from: <Available from: https://link.springer.com/article/10.1007/ s11119-005-6787-1 >. Accessed: Apr. 13, 2020. doi: 10.1007/ s11119-005-6787-1. https://link.springer.com/article/10.100... )
Woebbecke Index	WI	$= \frac{(g - b)}{(r - g)}$	WOEBBECKE et al. (1995WOEBBECKE, D. M. et al. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE, 38: 259-269, 1995. Available from: <Available from: https://elibrary.asabe.org/abstract.asp?aid=27838 >. Accessed: Feb. 25, 2020. https://elibrary.asabe.org/abstract.asp?... )

SV	DF	-------------NL---------------		------------KL--------------		---------------GY-----------------
Blocks	2	3.7950	^*	37.420	^*	1810519	^*
Nitrogen (N)	3	15.4022	^***	51.866	^***	19439535	^***
Potassium (K₂O)	3	0.7095	ns	15.726	ns	115619	ns
N versus K₂O	9	2.0585	ns	5.943	ns	651722	ns
Residue	30	0.9200		7.038		440954
CV (%)		3.47		9.26		11.47

SV	DF	-------------EXR------------		---------------NDI--------------		--------------VARI---------------
Blocks	2	0.00642	^***	277.850	^***	0.01515	^***
Nitrogen (N)	3	0.00328	^***	76.426	^***	0.00332	^***
Potassium (K₂O)	3	0.00004	ns	6.714	ns	0.00078	ns
N versus K₂O	9	0.00070	^*	16.464	^*	0.00068	ns
Residue	30	0.00024		5.409		0.00050
CV (%)		12.03		1.72		23.20