Spectral detection of nematodes in soybean at flowering growth stage using unmanned aerial vehicles

Arantes, Bruno Henrique Tondato; Moraes, Victor Hugo; Geraldine, Alaerson Maia; Alves, Tavvs Micael; Albert, Alice Maria; Silva, Gabriel Jesus da; Castoldi, Gustavo

doi:10.1590/0103-8478cr20200283

Acessibilidade / Reportar erro

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

CROP PROTECTION • Cienc. Rural 51 (5) • 2021 • https://doi.org/10.1590/0103-8478cr20200283 copy

Spectral detection of nematodes in soybean at flowering growth stage using unmanned aerial vehicles

Detecção espectral de nematóides na soja em estádio de florescimento por meio de veículos aéreos não-tripulados

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT:

Soybean is one of the main crop species grown in the world. However, there is a decline in productivity due to the various types of stress, including the nematodes Heterodera glycines and Pratylenchus brachyurus. The objectives were to determine the best spectral band for detecting H. glycines and P. brachyurus at the beginning of flowering (R1). Soil and root sampling was conducted at nine sampling sites in each of the five nematode-infested regions, totaling 45 sampling points. Flights were made at all regions using Phantom 4 Advanced, Sequoia and 14-band customized Sentera. For H. glycines, the red spectral band best explained the variability on soil and root nematode counts as well as the second stage of juveniles in soil. For P. brachyurus, Sentera RedEdge best explained the variability in root nematode counts and Sequoia NIR best explained soil juveniles. A multiple linear regression model using spectral data for detecting P. brachyurus and H. glycines improved R² compared to simple linear regressions. At flowering growth stage (R1), soybean spectral reflectance was associated with the number of H. glycines and P. brachyurus on soil and roots using low-cost and multispectral sensors.

Key words:
Glycine max; drones; remote sensing; precision farming; integrated management

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

Acompanhe os números deste periódico no seu leitor de RSS

[1] E-mail: tondatobh@gmail.com. ^*Corresponding author

Dependent variable nematode/agronomic (y)	Independent variable (x)	Regression equation	P-value	R²
Non-viable cyst	615 nm	$Y = - 43.4171 + 0.9169 \times x$	0.032	0.081
	RedEdge - Sentera (720 nm)	$Y = - 25.9480 + 0.7000 \times x$	0.041	0.072
	Nir - NDRE (840 nm)	$Y = - 34.2038 + 0.4268 \times x$	0.034	0.079
	Red - Phantom	$Y = - 87.5923 + 0.9258 \times x$	0.003	0.162
H. glycines in root	615 nm	$Y = - 4.9340 + 0.0827 \times x$	0.021	0.096
H. glycines in root	Red - Phantom	$Y = - 9.0733 + 0.0850 \times x$	0.001	0.197
H. glycines in soil	615 nm	$Y = - 55.8575 + 0.9485 \times x$	0.006	0.138
H. glycines in soil	Red - Phantom	$Y = - 93.7538 + 0.8870 \times x$	< 0.001	0.222
P. brachyurus in root	RedEdge - Sentera (720 nm)	$y = 1286.9880 - 14.7590 \times x$	0.007	0.137
	Nir - NDRE (840 nm)	$y = 1374.3350 - 8.2390 \times x$	0.011	0.121
	RedEdge - Sequoia (735 nm)	$Y = - 632.8505 + 0.0290 \times x$	0.024	0.091
P. brachyurus in soil	Air - Sequoia (790 nm)	$y = 99.8122 - 0.0024 \times x$	0.042	0.071
Plant height	615 nm	$y = 32.5222 - 0.1373 \times x$	0.018	0.102
	Red - Sentera (650 nm)	$y = 60.4020 - 0.3200 \times x$	< 0.001	0.422
	Blue - Sentera (446 nm)	$y = 48.6091 - 0.2725 \times x$	0.022	0.094
	Red - Phantom	$y = 37.7422 - 0.1260 \times x$	0.003	0.161
	RedEdge - Sequoia (735 nm)	$y = 12.9613 - 0.0003 \times x$	0.005	0.146
	Air - Sequoia (790 nm)	$y = 13.6545 - 0.0003 \times x$	0.015	0.107
Shoot dry mass (MSA)	615 nm	$y = 3.9011 - 0.0203 \times x$	0.045	0.068
	586-nm	$y = 5.0940 - 0.0177 \times x$	0.031	0.082
	Red - Sentera (650 nm)	$y = 7.5385 - 0.0431 \times x$	< 0.001	0.245
	Green - Sentera (548 nm)	$y = 6.3636 - 0.0259 \times x$	0.015	0.109
	Blue - Sentera (446 nm)	$y = 7.4970 - 0.0537 \times x$	0.008	0.129
	Red - Phantom	$y = 5.4740 - 0.0259 \times x$	< 0.001	0.236
	Green - Phantom	$y = 5.3136 - 0.016 \times x$	0.013	0.113
	Blue - Phantom	$y = 4.4609 - 0.0214 \times x$	< 0.001	0.210
	Green - Sequoia (550 nm)	$y = 3.7115 - 0.0001 \times x$	0.036	0.077
Root dry mass (MSR)	615 nm	$y = 10.1170 - 0.0634 \times x$	0.023	0.093
	586-nm	$y = 13.5265 - 0.0531 \times x$	0.019	0.100
	Red - Sentera (650 nm)	$y = 14.6932 - 0.0751 \times x$	0.030	0.083
	Green - Sentera (548 nm)	$y = 14.7055 - 0.0596 \times x$	0.046	0.067
	Blue - Phantom	$y = 9.2664 - 0.0367 \times x$	0.048	0.066
Shoot green mass (MVA)	586-nm	$y = 7.2453 - 0.0237 \times x$	0.026	0.089
	Red - Sentera (650 nm)	$y = 7.7008 - 0.0330 \times x$	0.043	0.070
	Green - Sentera (548 nm)	$y = 9.9035 - 0.0414 \times x$	0.002	0.176
	Blue - Sentera (446 nm)	$y = 10.1784 - 0.0688 \times x$	0.009	0.125
	Red - Phantom	$y = 6.6788 - 0.0249 \times x$	0.011	0.119
	Green - Phantom	$y = 8.2020 - 0.0264 \times x$	0.002	0.179
	Blue - Phantom	$y = 5.9044 - 0.0230 \times x$	0.007	0.135
	Green - Sequoia (550 nm)	$y = 5.6894 - 0.0001 \times x$	0.008	0.130
Root green mass (MVR)	615 nm	$y = 31.4041 - 0.2121 \times x$	0.006	0.138
	Red - Sentera (650 nm)	$y = 57.2280 - 0.3434 \times x$	< 0.001	0.253
	Blue - Sentera (446 nm)	$y = 50.3016 - 0.3554 \times x$	0.029	0.085
	Red - Phantom	$y = 40.8630 - 0.2072 \times x$	< 0.001	0.246
	Blue - Phantom	$y = 26.3539 - 0.1318 \times x$	0.011	0.120

Dependent variable	Mathematical model	P-value	R²
H. glycines in root	y = 8.4733 + (- 0.0988 × Red_Sentera) + (0.1330 × Red_Phantom) + (- 0.1292 × Green_Phantom) + (0.1081 × Blue_Phantom)	< 0.001	0.327
P. brachyurus in root	y = 1522.837 + (- 20.703 × RedEdge - Sentera) + (37.834 × red - NDVI) + (- 24.128 × Air - NDVI)	< 0.001	0.319