Assessment of maize hybrid water status using aerial images from an unmanned aerial vehicle

Lopes, Alzeneide da S.; Andrade Júnior, Aderson S. de; Bastos, Edson A.; Sousa, Carlos A. F. de; Casari, Raphael A. das C. N.; Moura, Magna S. B. de

doi:10.1590/1983-21252024v3711701rc

Acessibilidade / Reportar erro

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

Scientific Article • Rev. Caatinga 37 • 2024 • https://doi.org/10.1590/1983-21252024v3711701rc copy

Assessment of maize hybrid water status using aerial images from an unmanned aerial vehicle

Detecção do estado hídrico em híbridos de milho com imagens aéreas obtidas por aeronave remotamente pilotada

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

The objective of this work was to evaluate the potential of vegetation indices (VIs), obtained using aerial images from an unmanned aerial vehicle (UAV), for assessing water status of maize hybrids subjected to different water regimes under the soil and climate conditions of Teresina, Piauí, Brazil. Evaluations were carried out considering the application of five water regimes (WR) based on the crop evapotranspiration (ETc) (40%, 60%, 80%, 100%, and 120% of ETc) for three maize hybrids: BRS 3046 (conventional triple hybrid), BRS 2022 (conventional double hybrid), Status VIP3 (transgenic simple hybrid). A randomized block experimental design with four replications was used, in a 5×3 split-plot arrangement, consisting of WRs in the plots and maize hybrids in the subplots. A UAV was used for acquiring multispectral images. Eighteen VIs were evaluated and correlated with stomatal conductance (gs), leaf relative water content (RWC), and grain yield (GY). The VIs TCARI -RE and NDVI presented correlation with gs, whereas MNGRD and GCI presented correlation with RWC; therefore, they were considered promising for assessing the water status of maize plants. NDVI and WDRVI presented correlations with GY. Maps of NDVI, MNGRV, and WDRVI showed spatial correlation with gs, RWC, and GY measurements, respectively, in response to the applied WRs, denoting potential for assessing the water status of maize plants using aerial images from UAV.

Keywords:
Drone; Cultivar; Water stress; Vegetation index; Remote sensing

Layer (m)	OM	pH	P	K	Mg	Ca	Na	CEC	BS
Layer (m)	g kg^-1	H₂O	mg dm^-3	cmolc dm^-3					%
0.0-0.2	12.9	5.78	31.12	0.09	0.35	0.78	0.02	2.94	42.32
0.2-0.4	11.2	5.95	23.49	0.09	0.42	0.73	0.02	2.89	44.11

Layer (m)	Density (g cm^-3)	Sand	Silt	Clay	Өcc	Өpmp
Layer (m)	Density (g cm^-3)	------------------------- g kg^-1 -------------------------			(%, volume)
0.0-0.2	1.70	876.5	37.5	86.0	21.7	5.3
0.2-0.4	1.65	811.5	52.5	136.0	20.8	6.0

Period	DAS	40% ETc	60% ETc	80% ETc	100% ETc	120% ETc
Aug 05 to Sep 09, 2019	0–36	184.0	184.6	186.4	182.1	179.3
Sep 10 to Sep 24, 2019*	37–50	37, 2	57.0	74.2	91.6	108.8
Sep 25 to Nov 12 2019**	51– 99	114.8	165.2	210.7	272.3	330.5
Total	0–99	336.0	406.8	471.3	546.0	618.6

Vegetation index	Equation Mathematical	Reference
Green chlorophyll index	$G C I = \frac{R_{n}}{R_{g}} - 1$	Gitelson et al. (2005)GITELSON, A. A. et al. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32: 1-4, 2005.
Modified excess green	MEXG = 1.262R_g - 0.884R_r - 0.311R_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, 75: 337-346, 2011.
Modified normalized green red difference	$M N G R D = \frac{R_{g}^{2} - R_{r}^{2}}{R_{g}^{2} + R_{r}^{2}}$	Bendig et al. (2015)BENDIG, 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, 39: 79-87, 2015.
Normalized difference Red-Edge –	$N D R E = \frac{R_{n} - R_{R E}}{R_{n} + R_{R E}}$	Wang, Azzari and Lobell (2019)WANG, S.; AZZARI, G.; LOBELL, D. B. Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques. Remote Sensing of Environment, 222: 303-317, 2019.
Normalized difference Red-Edge index	$N D R E I = \frac{R_{R E} - R_{g}}{R_{R E} + R_{g}}$	Hassan et al. (2018)HASSAN, M. A. et al. Time-series multispectral indices from unmanned aerial vehicle imagery reveal senescence rate in bread wheat. Remote Sensing, 10: 809-828, 2018.
Normalized Difference Vegetation Index	$N D V I = \frac{R_{n} - R_{r}}{R_{n} + R_{r}}$	Gitelson et al. (2005)GITELSON, A. A. et al. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32: 1-4, 2005.
Normalized green red difference –	$N G R D = \frac{R_{g} - R_{r}}{R_{g} + R_{r}}$	Hamuda, Glavin and Jones (2016)HAMUDA, E.; GLAVIN, M.; JONES, E. A survey of image processing techniques for plant extraction and segmentation in the field. Computers and Electronics in Agriculture, 125: 184-199, 2016.
Pigment-specific normalized difference index	$P S N D = \frac{R n - R b}{R n + R b}$	Blackburn (1998)BLACKBURN, G. A. Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66: 273-285, 1998.
Red-Edge chlorophyll index	$R E C I = \frac{R_{n}}{R_{R E}} - 1$	Gitelson et al. (2005)GITELSON, A. A. et al. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32: 1-4, 2005.
Red green difference –	RGD =R_r - R_g	Sanjerehei (2014)SANJEREHEI, M. M. Assessment of spectral vegetation indices for estimating vegetation cover in arid and semiarid shrublands. Range Management & Agroforest, 35: 91-100, 2014.
Ratio vegetation index RVI	$R V I = \frac{R_{n}}{R_{r}}$	Tucker (1979)TUCKER, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8: 127-150, 1979.
Soil Adjusted Vegetation Index	$S A V I = \frac{1.5 (R_{n} - R_{r})}{(R_{n} + R_{r} + 0.5)}$	Zhong, Hu and Zhou (2019)ZHONG, L.; HU, L.; ZHOU, H. Deep learning based multi-temporal crop classification. Remote Sensing of Environment, 221: 430-443, 2019.
Transformed chlorophyll absorption in Nir index	TCARI = 3[(R_n - R_r) - 0.2(R_n - R_g)(R_n/R_r)]	Haboudane et al. (2002)HABOUDANE, D. et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81: 416-426, 2002.
TCARI/OSAVI index	$T C A R I - O S A V I = \frac{T C A R I}{O S A V I}$	Haboudane et al. (2002)HABOUDANE, D. et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81: 416-426, 2002.
Transformed chlorophyll absorption in Red-edge index	$T C A R I - R E = 3 [(R_{R E} - R_{r}) - 0.2 (R_{R E} - R_{g}) (\frac{R_{R E}}{R_{r}})]$	Daughtry et al. (2000)DAUGHTRY, C. S. T. et al. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74: 229-239, 2000.
Wide dynamic range vegetation index	$W D R V I = \frac{(0.12 R_{n}) - R_{r}}{(0.12 R_{n}) + R_{r}}$	Gitelson (2004)GITELSON, A. A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 161: 165-173, 2004.

Source of Variation	DF	gs	RWC	GY
Water regime (WR)	4	14995*	490.940***	93362560***
Block	3	5371	80.110	3813255
Error a	12	3309	40.720	495152
Hybrid (H)	2	2211^ns	107.850^ns	7644094***
RH×H	8	7003^ns	28.410^ns	6402201***
Error b	30	4873	41.640	787322
Total	59
CV WR (%)		22.97	8.50	15.23
CV H (%)		27.87	8.60	19.21

VI	WR	H	WR×H	VI	WR	H	WR×H
GCI	*	**	ns	RECI	***	ns	ns
MEXG	*	***	*	RVI	***	ns	ns
MNGRD	*	**	ns	SAVI	***	ns	ns
NDRE	***	***	ns	TCARI	***	***	ns
NRDEI	*	ns	ns	TCARI-OSAVI	***	***	ns
NDVI	**	ns	ns	RGD	ns	ns	ns
NGRD	*	***	ns	TCARI-RE	**	***	ns
PSND	***	ns	ns	WDRVI	***	**	ns

VI	gs	RWC	GY	VI	gs	RWC	GY
GCI	-0.860^**	-0.920**	-0.62ns	RDVI	0.722^ns	0.563^ns	0.911^**
MEXG	-0.479^ns	-0.214^ns	-0.737^ns	RVI	0.882^**	0.740^ns	0.949^**
MNGRD	0.872^**	0.925^**	0.635^ns	SAVI	0.720^ns	0.557^ns	0.909^**
NDRE	0.796^ns	0.603^ns	0.923^**	TCARI	-0.798^ns	-0.627^ns	-0.927^**
NDREI	0.169^ns	0.394^ns	0.487^ns	TCARI-OSAVI	-0.816^**	-0.653^ns	-0.934^**
NDVI	0.946**	0.875**	0.928^**	TCARI-RE	-0.928^**	-0.861^**	-0.939^**
NGRD	0.877^ns	0.916**	0.640^ns	RGD	-0.026^ns	-0.254^ns	0.288^ns
PSND	0.908^**	0.803 ^ns	0.920^**	WDRVI	0.919**	0.813**	0.948**

Universidade Federal Rural do Semi-Árido Avenida Francisco Mota, número 572, Bairro Presidente Costa e Silva, Cep: 5962-5900, Telefone: 55 (84) 3317-8297 - Mossoró - RN - Brazil
E-mail: caatinga@ufersa.edu.br

Acompanhe os números deste periódico no seu leitor de RSS

[1] *Corresponding author: <agro.neide@hotmail.com>