Predicting soybean grain yield using aerial drone images

Andrade Júnior, Aderson S. de; Silva, Silvestre P. da; Setúbal, Ingrid S.; Souza, Henrique A. de; Vieira, Paulo F. de M. J.; Casari, Raphael A. das C. N.

doi:10.1590/1807-1929/agriambi.v26n6p466-476

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Articles • Rev. bras. eng. agríc. ambient. 26 (6) • June 2022 • https://doi.org/10.1590/1807-1929/agriambi.v26n6p466-476 copy

Predicting soybean grain yield using aerial drone images¹ 1 Research developed at Embrapa Meio-Norte, Teresina, PI, Brazil

Predição da produtividade de grãos de soja utilizando imagens aéreas de drone

Authorship SCIMAGO INSTITUTIONS RANKINGS

HIGHLIGHTS:

Prediction models based on VIs using the red, NIR and red edge bands better explained the variability in soybean grain yield.

The variability of soybean grain yield was due more to water regimes than nitrogen supplementation.

The soybean grain yield prediction model generated with the EVI-2 VI showed greater accuracy and spatial randomness.

ABSTRACT

This study aimed to evaluate the ability of vegetation indices (VIs) obtained from unmanned aerial vehicle (UAV) images to estimate soybean grain yield under soil and climate conditions in the Teresina microregion, Piaui state (PI), Brazil. Soybean cv. BRS-8980 was evaluated in stage R5 and submitted to two water regimes (WR) (100 and 50% of crop evapotranspiration - ETc) and two N levels (with and without N supplementation). A randomized block design in a split-plot scheme was used, in which the plots were the water regimes and the subplots N levels, with five replicates. Each plot contained twenty 4.5 m-long rows, spaced 0.5 m apart, with a total area of 45 and 6 m² study area for grain yield evaluations. Twenty VIs obtained from multispectral aerial images were evaluated and correlated with grain yield measurements in the field. Pearson’s correlation, linear regression, and spatial autocorrelation (Global and Local Moran’s I) were used to analyze the performance of the VIs in predicting grain yield. The R², RMSE and nRMSE indices were used to validate the linear regression models. The prediction model based on EVI-2 exhibited high spatial randomness for all the treatments, and smaller prediction errors of 149.68 and 173.96 kg ha^-1 (without and with N supplementation, respectively).

Key words:
Glycine max L.; remotely piloted aircraft; vegetation indices; autocorrelation; Moran’s I

Layer (m)	Sand		Fine Sand			Silt			Clay		Textural classification
Layer (m)	(g kg^-1)										Textural classification
0-0.20	267.3		470.6			116.0			146.1		Sandy loam
0.20-0.40	231.6		424.0			105.5			238.9		Sandy clay loam
	P (mg dm^-3)	K		Ca	Mg		H + Al	SB		CEC		OM (dag kg^-1)	S (%)	pH (CaCl₂)
	P (mg dm^-3)	(cmol_c dm^-3)										OM (dag kg^-1)	S (%)	pH (CaCl₂)
0-0.20	10.15	0.23		2.40	0.83		1.18	3.45		4.63		1.03	74.5	5.50
0.20-0.40	5.41	0.22		2.63	0.83		1.34	3.68		5.01		0.91	73.4	5.38

WR (% ETc)	S-OT	OT-FD	OT-ET	ET-EIT	Season
50	125.3	84.0	90.6	56.1	356.0
100	127.1	159.4	167.7	57.7	511.9

Bands/Indices	Acronym	Equation	Reference
Chlorophyll index red	CI-RED	$\frac{R_{n}}{R_{r}} - 1$	Gitelson et al. (2005Gitelson, A. A.; Viña, A.; Ciganda, V.; Rundquist, D. C.; Arkebauer, T. J. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, v.32, p.1-4, 2005. https://doi.org/10.1029/2005GL022688 https://doi.org/10.1029/2005GL022688... )
Chlorophyll vegetation index	CVI	$\frac{R_{n} R_{r}}{R_{g}^{2}}$	Vincini et al. (2008Vincini, M.; Frazzi, F.; D’Alessio, P. A broad-band leaf chlorophyll vegetation index at the canopy scale. Precision Agriculture, v.9, p.303-319, 2008. https://doi.org/10.1007/s11119-008-9075-z https://doi.org/10.1007/s11119-008-9075-... )
Enhanced vegetation index	EVI	$\frac{2.5 (R_{n} - R_{r})}{R_{n} + 6 R_{r} - 7.5 R_{b} + 1}$	Gitelson et al. (2005Gitelson, A. A.; Viña, A.; Ciganda, V.; Rundquist, D. C.; Arkebauer, T. J. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, v.32, p.1-4, 2005. https://doi.org/10.1029/2005GL022688 https://doi.org/10.1029/2005GL022688... )
Two-band enhanced vegetation index	EVI-2	$\frac{2.5 (R_{n} - R_{r})}{R_{n} + 2.4 R_{r} + 1}$	Jiang et al. (2008Jiang, Z.; Huete, A.; Miura, T.; Didan, K. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment , v.112, p.3833-3845, 2008. https://doi.org/10.1016/j.rse.2008.06.006 https://doi.org/10.1016/j.rse.2008.06.00... )
Green chlorophyll index	GCI	$\frac{R_{n}}{R_{g}} - 1$	Gitelson et al. (2005Gitelson, A. A.; Viña, A.; Ciganda, V.; Rundquist, D. C.; Arkebauer, T. J. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, v.32, p.1-4, 2005. https://doi.org/10.1029/2005GL022688 https://doi.org/10.1029/2005GL022688... )
Green normalized difference vegetation	GNDVI	$\frac{R_{n} - R_{g}}{R_{n} + R_{g}}$	Hunt & Daughtry (2018Hunt, E. R.; Daughtry, C. S. T. What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? International Journal of Remote Sensing, v.39, p.5345-5376. 2018. https://doi.org/10.1080/01431161.2017.1410300 https://doi.org/10.1080/01431161.2017.14... )
Green ratio vegetation index	GRVI	$\frac{R_{n}}{R_{g}}$	Sripada et al. (2006Sripada, R. P.; Heiniger, R. W.; White, J. G.; Meijer, A. D. Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy Journal, v.98, p.968-977, 2006. https://doi.org/10.2134/agronj2005.0200 https://doi.org/10.2134/agronj2005.0200... )
Modified excess green	MEXG	$1.262 R_{g} - 0.884 R_{r} - 0.311 R_{b}$	Burgos-Artizzu et al. (2011Burgos-Artizzu, X. P.; Ribeiro, A.; Guijarro, M.; Pajares, G. Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, v.75, p.337-346, 2011. https://doi.org/10.1016/j.compag.2010.12.011 https://doi.org/10.1016/j.compag.2010.12... )
Modified normalized green red difference	MNGRD	$\frac{R_{g}^{2} - R_{r}^{2}}{R_{g}^{2} + R_{r}^{2}}$	Bendig et al. (2015Bendig, J.; Yu, K; Aasen, H.; Bolten, A.; Bennertz, S.; Broscheit, J.; Gnyp, M. L.; Bareth, G. 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. https://doi.org/10.1016/j.jag.2015.02.012 https://doi.org/10.1016/j.jag.2015.02.01... )
Normalized difference Red-Edge	NDRE	$\frac{R_{n} - R_{R E}}{R_{n} + R_{R E}}$	Wang et al. (2014Wang, L.; Tian, Y.; Yao, X.; Zhu, Y.; Cao, W. Predicting grain yield and protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images. Field Crops Research, v.164, p.178-188, 2014. https://doi.org/10.1016/j.fcr.2014.05.001 https://doi.org/10.1016/j.fcr.2014.05.00... )
Normalized difference vegetation index	NDVI	$\frac{R_{n} - R_{r}}{R_{n} + R_{r}}$	Gitelson et al. (2005Gitelson, A. A.; Viña, A.; Ciganda, V.; Rundquist, D. C.; Arkebauer, T. J. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, v.32, p.1-4, 2005. https://doi.org/10.1029/2005GL022688 https://doi.org/10.1029/2005GL022688... )
Normalized green red difference	NGRD	$\frac{R_{g} - R_{r}}{R_{g} + R_{r}}$	Hamuda et al. (2016Hamuda, E.; Glavin, M.; Jones, E. A survey of image processing techniques for plant extraction and segmentation in the field. Computers and Electronics in Agriculture , v.125, p.184-199, 2016. https://doi.org/10.1016/j.compag.2016.04.024 https://doi.org/10.1016/j.compag.2016.04... )
Optimized Soil Adjusted Vegetation Index	OSAVI	$\frac{R_{n} - R_{r}}{R_{n} + R_{r} + 0.16}$	Roundeaux et al. (1996Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment , v.55, p.95-107, 1996. https://doi.org/10.1016/0034-4257(95)00186-7 https://doi.org/10.1016/0034-4257(95)001... )
Pigment-specific normalized difference index	PSND	$\frac{R_{n} - R_{b}}{R_{n} + R_{b}}$	Blackburn (1998Blackburn, G. A. Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing of Environment, v.66, p.273-285, 1998. https://doi.org/10.1016/S0034-4257(98)00059-5 https://doi.org/10.1016/S0034-4257(98)00... )
Renormalized Difference Vegetation Index	RDVI	$\frac{R_{n} - R_{r}}{{(R_{n} + R_{r})}^{0.5}}$	Roujean & Breon (1995Roujean, J. L.; Breon, F. M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, v.51, p.375-384, 1995. https://doi.org/10.1016/0034-4257(94)00114-3 https://doi.org/10.1016/0034-4257(94)001... )
Red-Edge chlorophyll index	RECI	$\frac{R_{n}}{R_{R E}} - 1$	Gitelson et al. (2005Gitelson, A. A.; Viña, A.; Ciganda, V.; Rundquist, D. C.; Arkebauer, T. J. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, v.32, p.1-4, 2005. https://doi.org/10.1029/2005GL022688 https://doi.org/10.1029/2005GL022688... )
Ratio vegetation index	RVI	$\frac{R_{n}}{R_{r}}$	Gitelson et al. (2005Gitelson, A. A.; Viña, A.; Ciganda, V.; Rundquist, D. C.; Arkebauer, T. J. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, v.32, p.1-4, 2005. https://doi.org/10.1029/2005GL022688 https://doi.org/10.1029/2005GL022688... )
Soil Adjusted Vegetation Index	SAVI	$\frac{1.5 (R_{n} - R_{r})}{(R_{n} + R_{r} + 0.5)}$	Zhong et al. (2019Zhong, L.; Hu, L.; Zhou, H. Deep learning based multi-temporal crop classification. Remote Sensing of Environment, v.221, p.430-443, 2019. https://doi.org/10.1016/j.rse.2018.11.032 https://doi.org/10.1016/j.rse.2018.11.03... )
Simplified canopy chlorophyll content index	SCCCI	$\frac{N D R E}{N D V I}$	Raper & Varco (2015Raper, T. B.; Varco, J. J. Canopy-scale wavelength and vegetative index sensitivities to cotton growth parameters and nitrogen status. Precision Agriculture, v.16, p.62-76, 2015. https://doi.org/10.1007/s11119-014-9383-4 https://doi.org/10.1007/s11119-014-9383-... )
Triangular greenness index	TGI	$R_{g} - (0.39 R_{r}) - (0.61 R_{b})$	Hunt et al. (2011Hunt, E. R.; Daughtry, C. S. T.; Eitel, J. U. H.; Long, D. S. Remote sensing leaf chlorophyll content using a visible band index. Agronomy Journal, v.103, p.1090-1099, 2011. https://doi.org/10.2134/AGRONJ2010.0395 https://doi.org/10.2134/AGRONJ2010.0395... )

VI#	r	VI	r	VI	r	VI	r	VI	r
CI-RED	0.789***	GCI	0.802***	MNGRD	-0.032^ns	OSAVI	0.828***	RVI	0.789***
CVI	0.709***	GNDVI	0.781***	NDRE	0.803***	PSND	0.707***	SAVI	0.837***
EVI	0.839***	GRVI	0.802***	NDVI	0.757***	RDVI	0.839***	SCCCI	0.793***
EVI-2	0.845***	MEXG	-0.470**	NGRD	-0.074^ns	RECI	0.816***	TGI	-0.542**

IV#	Metrics		N0	N1	All	IV#	N0	N1	All
EVI	Moran’s I		-0.01449^ns	-0.01449*	-0.00719^ns	RDVI	-0.01449^ns	-0.01449^ns	-0.00719^ns
	RMSE	Q1	84.86	79.81	82.34		85.27	69.98	77.62
		Med.	147.46	177.39	162.42		150.65	177.12	163.88
		Q3	188.52	233.13	214.27		191.12	234.47	210.05
	nRMSE	Q1	3.88	4.87	4.27		5.19	5.67	5.40
		Med.	8.19	10.53	9.36		15.17	20.72	17.94
		Q3	10.04	12.75	11.91		16.91	30.69	23.84
EVI-2	Moran’s I		-0.01449***	-0.01449*	-0.00719*	SAVI	-0.01449***	-0.01449^ns	-0.00719^ns
	RMSE	Q1	73.82	71.26	72.81		88.36	77.02	82.69
		Med.	149.68	173.96	161.82		150.87	179.64	165.25
		Q3	186.77	237.07	213.30		192.01	239.00	217.94
	nRMSE	Q1	4.23	4.27	4.25		3.94	5.09	4.43
		Med.	8.31	10.24	9.27		8.39	10.73	9.56
		Q3	10.19	13.39	11.81		10.40	13.20	12.46

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E-mail: revistagriambi@gmail.com

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

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Predicting soybean grain yield using aerial drone images1 1 Research developed at Embrapa Meio-Norte, Teresina, PI, Brazil

Predição da produtividade de grãos de soja utilizando imagens aéreas de drone

HIGHLIGHTS:

ABSTRACT

Predicting soybean grain yield using aerial drone images¹ 1 Research developed at Embrapa Meio-Norte, Teresina, PI, Brazil