Estimation of biometric, physiological, and nutritional variables in lettuce seedlings using multispectral images

Martins, George D.; Silva, Onésio F. da; Carmo, Glecia J. dos S.; Castoldi, Renata; Santos, Ludymilla C. S.; Charlo, Hamilton C. de O.

doi:10.1590/1807-1929/agriambi.v25n10p689-695

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Articles • Rev. bras. eng. agríc. ambient. 25 (10) • Oct 2021 • https://doi.org/10.1590/1807-1929/agriambi.v25n10p689-695 copy

Estimation of biometric, physiological, and nutritional variables in lettuce seedlings using multispectral images¹ 1 Research developed at Uberaba, MG, Brazil

Estimativa de variáveis biométricas, fisiológicos e nutricionais em mudas de alface usando câmara multiespectral

Authorship SCIMAGO INSTITUTIONS RANKINGS

HIGHLIGHTS:

Multispectral imaging, using a low-cost camera, makes it possible to assess the agronomic characteristics of lettuce.

Predictive models can be obtained using parametric and non-parametric algorithms based on machine-learning approaches.

Specific wavelengths of the reflective spectrum of lettuce are important plant quality indicators.

ABSTRACT

The formation of seedlings is one of the most important phases of lettuce cultivation. Therefore, any strategy that aims to obtain high-quality seedlings can increase productivity. One of these strategies is the prediction of morphophysiological attributes based on optical properties. The objective of this study was to quantitatively estimate the biometric variables of lettuce from parametric and non-parametric models based on the response of multispectral camera images. The experiment was conducted in a greenhouse in the municipality of Uberaba, Minas Gerais State, Brazil. Twenty days after sowing, multispectral images of the plants were captured using a MAPIR Survey 3 camera. To compose the estimation models, along with the original bands of the camera, the multispectral vegetation indices were calculated using the calibrated original camera bands. Bands B₅₅₀, B₆₆₀, and B₈₅₀ and the near-infrared indices contributed significantly to estimating the physiological variable models, with B₈₅₀ contributing the most to the biometric and nutritional variables. From the near-infrared band (B₈₅₀) and derived indices, it was possible to estimate all the agronomic variables from the models generated by the M5 algorithm, with an accuracy of up to 1.6% for the maximum quantum yield. Thus, it is possible to quantify the biometric, physiological, and nutritional variables of lettuce using a multispectral camera. Among the Mapir camera bands, B₆₆₀ exhibited the greatest variability, showing that the red range was the most sensitive.

Key words:
Lactuca sativa; morphophysiological variables; prediction models

Index	Equation	Eq.	Reference
Simple ratio	$S R = (B_{850}) / (B_{660})$	(2)	Birth & McVey (1968)Birth, G. S.; Mcvey, G. R. Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal, v.60, p.640-643, 1968. https://doi.org/10.2134/agronj1968.00021962006000060016x https://doi.org/10.2134/agronj1968.00021...
Normalized difference vegetation index	$N D V I = (B_{850} - B_{660}) / (B_{850} + B_{660})$	(3)	Rouse et al. (1973)Rouse, J. W.; Haas, R. H.; Well, J. A.; Deering, D. W. Monitoring vegetation systems in the great plains with ERTS. Proceedings, v.1, p.309-317, 1973.
Green normalized difference vegetation index	$G N D V I = (B_{850} - B_{550}) / (B_{850} + B_{550})$	(4)	Gitelson et al. (1996)Gitelson, A. A.; Kaufman, Y. J.; Merzlyak, M. N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, v.58, p.289-298, 1996. https://doi.org/10.1016/S0034-4257(96)00072-7 https://doi.org/10.1016/S0034-4257(96)00...

Variables	Means	Standard deviations	CV (%)
B₈₅₀	252.89	6.59	2.26
B₅₅₀	244.06	1.96	0.81
B₆₆₀	225.10	12.05	5.35
Biometric
NF15	3.57	0.41	11.45
SDM	0.08	0.02	25.30
RDM	0.02	0.006	27.06
Area	32.35	10.41	32.17
Physiological
F₀	7,281.91	696.98	9.57
F_m	39,588.33	4,184.01	10.57
F_v	32,306.42	3,603.00	11.15
F_v/F_m	0.82	0.01	1.25
ABS/RC	2,309.99	148.94	6.45
TRo/RC	1,880.77	110.03	5.85
ETo/RC	446.30	277.55	62.19
DIo/RC	0.43	0.05	10.62
Chlorophyll a	17.36	1.55	8.95
Nutritional
N	13.39	2.34	17.47
P	6.49	0.80	12.37
K	31.54	3.39	10.75
Ca	9.23	1.05	11.37
Mg	2.35	0.16	7.01
S	1.20	0.18	15.07

Variables	Model	R² (%)
Biometric
Number of leaves at 15 DAE	$N F 15 = - 3.44 \times G N D V I + 3.7$	22
Shoot dry mass (g plant^-1)	$S D M = - 0.01 \times B_{850} + 2.7$	18
Root dry mass (g plant^-1)	$R D M = 0.00002 \times B_{850} + 0.2$	20
Leaf area (cm² plant^-1)	$A r e a = - 5.7 \times B_{850} + 1341.6$	2
Physiological
Initial fluorescence	$F_{0} = - 99 + 7 \times B_{850} + 32647.2$	19
Maximum fluorescence	$F_{m} = 328.48 \times B_{550} - 39469.7$	26
Variable fluorescence	$F_{v} = 315.06 \times B_{550} - 43630.2$	15
Maximum quantum yield	$F_{v} / F_{m} = 0.0095 \times B_{850} - 0.16 \times G N D V I - 1.58$	43
Absorption flow/center of reaction	$A B S / R C = - 47.51 \times B_{850} + 14330.4$	7.8
Captured energy flow/center of reaction	$T R o / R C = 33.67 \times B_{550} - 7735.06$	12
Electron transport flow/center of reaction	$E T o / R C = 33.67 \times B_{660} - 7735.06$	15
Non-photochemical energy flow/center of reaction	$D i o / R C = - 0.0336 \times B_{850} + 0.5251 \times G N D V I + 8.909$	34
Chlorophyll a index	$C h l o r o p h y l l a = - 0.054 \times B_{660} + 29.4$	15
Nutritional
Nitrogen leaf concentrations (g kg^-1)	$N = 1.33 \times B_{850} - 323.42$	11
Phosphorus leaf concentrations (g kg^-1)	$P = 0.46 \times B_{850} - 41.1 \times G N D V I - 21.31 \times S R - 89.07$	58
Potassium leaf concentrations (g kg^-1)	$K = 1.18 \times B_{850} - 269.55$	14
Calcium leaf concentrations (g kg^-1)	$C a = 0.18 \times B_{850} - 37.37$	10
Magnesium leaf concentrations (g kg^-1)	$M g = 0.06 \times B_{850} - 12.28$	16
Sulfur leaf concentrations (g kg^-1)	$S = 0.06 \times B_{850} - 14.62$	12.35

Variables	Neural network		M5
Variables	RMSE	RMSE%	RMSE	RMSE%
Biometric
NF15	0.421	11.79	0.382	10.7
SDM	0.022	27	0.025	32
RDM	0.005	15.6	0.007	22
Area	11.94	43	12.2	44
Physiological
F₀	742	10.2	826	11.38
F_m	4700	12.5	5253	14.26
F_v	4087	13.37	4538	14.85
F_v/F_m	0.013	1.7	0.012	1.6
ABS/RC	154	6.74	148.32	6.5
TRo/RC	112.38	6.06	107	5.8
ETo/RC	300	77	265	63
DIo/RC	0.050	12	0.052	12.1
Chlorophylla	1.305	5.9	1.31	7.5
Nutritional
N	3.4	23.5	3.19	21.9
P	1.11	16.2	1.07	15.6
K	3.11	9.2	3.32	10.7
Ca	1.09	11.2	1.34	13.7
Mg	0.21	8.6	0.18	7.5
S	0.15	12.4	0.14	11.5

Unidade Acadêmica de Engenharia Agrícola Unidade Acadêmica de Engenharia Agrícola, UFCG, Av. Aprígio Veloso 882, Bodocongó, Bloco CM, 1º andar, CEP 58429-140, Campina Grande, PB, Brasil, Tel. +55 83 2101 1056 - Campina Grande - PB - Brazil
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[1] * Corresponding author - E-mail: rcastoldi@ufu.br

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Estimation of biometric, physiological, and nutritional variables in lettuce seedlings using multispectral images1 1 Research developed at Uberaba, MG, Brazil

Estimativa de variáveis biométricas, fisiológicos e nutricionais em mudas de alface usando câmara multiespectral

HIGHLIGHTS:

ABSTRACT

Estimation of biometric, physiological, and nutritional variables in lettuce seedlings using multispectral images¹ 1 Research developed at Uberaba, MG, Brazil