© The Authors, 2021, Published by the Universidad del Zulia*Corresponding author: jonathanchris.e@gmail.com
Jonathan C. Espinoza Delgado
1*
Henry A. Pacheco Gil
2
Rev. Fac. Agron. (LUZ). 2022, 39(1): e223919
ISSN 2477-9407
DOI: https://doi.org/10.47280/RevFacAgron(LUZ).v39.n1.19
Crop Production
Associate editor: Professor Evelin Perez
Keywords:
EBEE SQ
Sequoia
Chlorophyll Index multispectral
Visible spectrum
Use of an unmanned aerial vehicle as an alternative to assess the nutritional status of the
cotton crop
Uso de un vehículo aéreo no tripulado como alternativa para evaluar el estado nutricional del cultivo
de algodón
Uso de veículo aéreo não tripulado como alternativa para avaliação do estado nutricional da cultura
do algodão
1*
Maestría en Ingeniería Agrícola, Instituto de Postgrado,
Universidad Técnica de Manabí, Ecuador
2
Facultad de Ingeniería Agrícola, Universidad Técnica de
Manabí, Ecuador.
Received: 14-04-2021
Accepted: 05-12-2021
Published: 24-02-2022
Abstract
The use of unmanned aerial vehicles in photogrammetric studies
allows obtaining spatial data in short periods of time and with high spatial
resolution. In the research, multispectral images were processed for the
study of nutritional conditions of the cotton crop (Gossypium hirsutum).
An experimental design of the crop was developed, with different doses
and nitrogen sources, in a factorial arrangement with 16 treatments and
4 repetitions in plots completely distributed at random. The EBEE SQ
agricultural drone, equipped with the Parrot Sequoia camera, was used and a
photogrammetric ight was planned, with the Emoticon AG software, which
was synchronized with the drone to establish the ight parameters and capture
the reectance information of the visible spectrum, infrared and red border.
The captured images were processed with the PIX4D Mapper software to
generate the orthophoto and the 4 spectral bands used in the calculation of
the chlorophyll index. Using map algebra tools from ArcGIS software on the
results obtained, an analysis of variance was performed with the ANOVA
model. With the calculated indices it was possible to show differences in the
vigor of the crop depending on the treatments. The analysis of the results
showed signicant differences in the spectral response of the cotton crop
fertilized with different sources (urea, pine nut cake, hen manure and bovine
manure) and nitrogen doses (50, 100, 150 and 200 N kg.ha
-1
). Urea treatment
at the 150 dose of N kg.ha
-1
showed the best spectral response.
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2022, 39(1): e223919. January - March. ISSN 2477-9407.
2-7 |
Resumen
El uso de los vehículos aéreos no tripulados en estudios
fotogramétricos permite la obtención de datos espaciales en cortos
periodos de tiempo y de alta resolución espacial. En la investigación se
procesaron imágenes multiespectrales para el estudio de condiciones
nutricionales del cultivo de algodón (Gossypium hirsutum). Se
desarrolló un diseño experimental del cultivo, con diferentes dosis y
fuentes nitrogenadas, en un arreglo factorial con 16 tratamientos y 4
repeticiones en parcelas distribuidas completamente al azar. Se utilizó
el dron agrícola EBEE SQ, equipado con la cámara Parrot Sequoia, y
se planicó un vuelo fotogramétrico, con el software Emoticón AG,
mismo que se sincronizó con el dron para establecer los parámetros
de vuelo y capturar la información de reectancia del espectro visible,
infrarrojo y borde rojo. Las imágenes capturadas se procesaron con
el software PIX4D Mapper para generar la ortofoto y las 4 bandas
espectrales usadas en el cálculo del índice de clorola. Utilizando
herramientas de algebra de mapas del software ArcGIS sobre los
resultados obtenidos, se ejecutó un análisis de varianza con el modelo
ANOVA. Con los índices calculados se pudo evidenciar diferencias
en la vigorosidad del cultivo en función de los tratamientos. El
análisis de los resultados mostró diferencias signicativas en la
respuesta espectral del cultivo de algodón fertilizado con diferentes
fuentes (urea, torta de piñón, gallinaza y estiércol bovino) y dosis
nitrogenadas (50, 100, 150 y 200 N kg.ha
-1
). El tratamiento de urea en
la dosis de 150 N kg.ha
-1
mostro la mejor respuesta espectral.
Palabras clave: EBEE SQ, Sequoia, índice de clorola,
multiespectral, espectro visible.
Resumo
A utilização de veículos aéreos não tripulados em estudos
fotogramétricos permite a obtenção de dados espaciais em curtos
períodos de tempo e com alta resolução espacial. Na pesquisa,
imagens multiespectrais foram processadas para o estudo das
condições nutricionais da cultura do algodão (Gossypium hirsutum).
Foi desenvolvido um delineamento experimental da cultura, com
diferentes doses e fontes de nitrogênio, em arranjo fatorial com
16 tratamentos e 4 repetições em parcelas totalmente distribuídas
ao acaso. Foi utilizado o drone agrícola EBEE SQ, equipado com
a câmera Parrot Sequoia, e planejado um voo fotogramétrico, com
o software Emoticon AG, que foi sincronizado com o drone para
estabelecer os parâmetros de voo e capturar as informações de
reetância do espectro visível., borda infravermelha e vermelha.
As imagens capturadas foram processadas com o software PIX4D
Mapper para gerar a ortofoto e as 4 bandas espectrais utilizadas
no cálculo do índice de clorola. Usando ferramentas de álgebra
de mapas do software ArcGIS nos resultados obtidos, uma análise
de variância foi realizada com o modelo ANOVA. Com os índices
calculados foi possível mostrar diferenças no vigor da cultura em
função dos tratamentos. A análise dos resultados mostrou diferenças
signicativas na resposta espectral da cultura do algodão fertilizado
com diferentes fontes (uréia, torta de pinhão, esterco de galinha e
esterco bovino) e doses de nitrogênio (50, 100, 150 e 200 N kg.ha
-1
).
O tratamento com uréia na dose de 150 N kg.ha
-1
apresentou a melhor
resposta espectral.
Palavras chave: EBEE SQ, Sequoia, número de clorola,
multiespectral, espectro visível.
Introduction
Remote sensing based on satellites and unmanned aircraft reduces
the problem in the scarce implementation of technological alternatives
for planning in agriculture. This technology, in response to the
various physiological and nutritional problems presented by crops,
allows improving the planning of agricultural activities, predicting
damage and making appropriate decisions in situations that affect
their development (Macías-Duarte et al., 2021).
The images of the unmanned aerial vehicles, equipped with
a multispectral camera, capture spectral images that are useful for
agricultural purposes in estimating the efciency of some treatments
and their effect on the phenological development of the crop (Burbano
and Peñaranda, 2020). The concentration of chlorophyll in the leaf
can vary depending on growth stage of the plant and proportional to
the amount of nitrogen it has (Ledesma et al., 2020).
The chlorophyll index is applied to estimate the total amount of
chlorophyll in plants and is generally calculated from the reectance
in the green, red and red edge bands of the electromagnetic spectrum.
These bands respond to slight variations in the amount of chlorophyll
and are consistent for most types of plants. The cell structure of plants
tends to reect waves within this spectral range, resulting in more
reected light; therefore, the higher the reection, the greener the area
will be, indicating the level of vigor of the crop, allowing, in turn, to
identify the affected areas and respond in a timely manner (Prando et
al., 2019).
RGB and multispectral data have been used to estimate the
chlorophyll index (Cl), which are based on the random application of
nitrogen in crops. With the application of remote sensing techniques
and with the support of multispectral images, to analyze the
morphological and nutritional conditions that the human eye cannot
easily observe, it is intended to support a technied agricultural
system. The cameras are managed by geographic information
systems and aerial tools, which establish parameters in the crops for
their better management (Kharuf-Gutierrez et al., 2018), hence the
objective of this research was to process multispectral images for the
study of nutritional conditions of cotton cultivation.
Materials and methods
Location of the study: The study area was the central coastal
region of Ecuador, in the experimental campus “La Teodomira” of the
Universidad Técnica de Manabí, Santa Ana Canton, Lodana parish,
North coordinates 9870040 and East 568460 at 56 m.a.s.l. (gure 1).
Figure 1. Location of the study área and cotton plots (scientic
name) in the experimental campus of the Universidad
Técnica de Manabí, Ecuador.
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Espinoza and Pacheco. Rev. Fac. Agron. (LUZ). 2022, 39(1): e223919
3-7 |
Vegetal material. Cotton cultivation (Gossypium hirsutum)
was evaluated in a total area of 2,688 m
2
, divided into 64 plots of
6 m
2
each, with a 3 % slope. Sowing was carried out at the end of
November 2019, with a separation of 1 m between rows and 0.4 m
between plants, as well as a single plant per planting point.
Flight plan. The photogrammetric ight was programmed through
the “EMOTION AG” software, considering the parameters detailed
in table 1 and gure 2.
Table 1. Parameters established by the “EMOTION AG” software
applied in the execution of the ight plan.
Information Emotion AG
Camera RGB (16 Mpx) + Multispectral (1.2 Mpx)
Type Sequoia 1.7.1
Image size (cm/pixel) 11.00 cm.px
-1
Flight time (s) 15:47 min
Flight area 24.2 ha
Longitudinal overlap
(%)
80 %
Transverse overlap (%) 70 %
Flight height (m) 150 m
Flight speed (m.s
-1
) 4 m.seg
-1
Figure 2. Flight planning in the study area.
Capture and processing of multispectral images. The
photogrammetric mission was carried out with the EBEE SQ
unmanned aerial vehicle, instrumented with the “Parrot Sequoia”
multispectral camera. Three hundred and seventy nine images were
captured with a pixel size of 11.00 cm, using the JPEG format for
the RBG image and the multispectral bands “GREEN, RED, NIR,
EDGE”, considering the performance of the geometry and radiometry
of multispectral images of the camera (Parrot Drone SAS, 2020).
The images from the Parrott Sequoia camera (Pix4D SA., 2019)
were processed by the Pix4D photogrammetry program, with which
georeferenced 2D and 3D digital models, orthomosaics and high-
precision separated spectral bands were created.
Algebraic operations in GIS. With the multispectral bands, and
using the equations proposed by Zhou et al. (2019), four chlorophyll
indices were calculated (table 2), through the map algebra tools of the
ArcGIS software called “raster calculator”, which allows mathematical
operations to be performed between the les, generating a le with
the values of the indices for each pixel of the image (ArcGIS, 2020)
that contains the sampling of 20 points in each treatment; for this, a
polygon-type vector Shape layer was created with which the analysis
areas of each of the areas where the treatments were applied were
delimited, according to the experimental design. Once the limits of
each treatment on the chlorophyll indices were established, a point-
type vectorial shape was created.
The value of the indices were obtained for each point, using the
ArcGIS Spatial Analyst Extract Multi Values to Points tool, which
extracts cell values at specied locations in a point feature class from
one or more rasters and records the values in the attribute table of the
point feature class (ArcGis. 2020), in order to obtain a reliable value
of each treatment (Marín et al., 2018). Finally, the table of attributes
of the vector shape layer of the point type was extracted, where the
maximum and minimum values were obtained, exported as an .xlsx
format le that was executed with the Excel program, showing all
the points with their values on each chlorophyll index for analysis of
variance.
Table 2. Chlorophyll indices used for the evaluation of cotton
cultivation in the study area.
Index Formula
“Simple Relationship Red Border” (Red Border) / (Red)
“Red Simple Proportion” (Near Infrared) / (Red)
“Reectance Spectra Ratio Analysis
(RARSc)”
(Red Border) / (Green)
“Normalized Difference Red Edge
Index (NDRE)”
(Red Border - Red) / (Red
Border + Red)
Source: Zhou et al. (2019).
From the division of zones, a new layer in .shp format of polygon
type was generated using ArcGIS tools, which allowed creating the
delimitations of the new plots based on the experimental design.
Each formula was used independently, differentiating each
treatment according to the experimental design proposed and
implemented in the ArcGIS software, using the polygons. Each
operation applied in the crop presents different minimum and
maximum levels of reectance, these being undened by the different
bands and radios used, do not have an established minimum and
maximum limit, so it is estimated that the higher the maximum levels,
the higher the chlorophyll index and vice versa.
Subsequently, the ArcGIS “zonal statistics” tool was applied
to generate statistics of the chlorophyll indices in each of the plots
(Bartesaghi et al., 2018).
Design and statistical analysis. The design was in completely
randomized blocks, with a 4*4 factorial arrangement, differentiated in
sources and fertilization doses, as shown in table 3. Four nitrogenous
sources were used (bovine manure, hen manure, pine nut cake and
urea) and four doses (50, 100, 150, 200 N kg.ha
-1
), for a total of 16
fertilization treatments.
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2022, 39(1): e223919. January - March. ISSN 2477-9407.
4-7 |
Table 3. Doses, sources and estimation of nitrogenous
fertilization used in the design of treatments.
Treatment Dose (N kg.ha
-1
) Source Estimation
T1 50 Bovine manure Very low
T2 100 Bovine manure Short
T3 150 Bovine manure High
T4 200 Bovine manure Very high
T5 50 Hen manure Very low
T6 100 Hen manure Short
T7 150 Hen manure High
T8 200 Hen manure Very high
T9 50 Pine nut cake Very low
T10 100 Pine nut cake Short
T11 150 Pine nut cake High
T12 200 Pine nut cake Very high
T13 50 Urea Very low
T14 100 Urea Short
T15 150 Urea High
T16 200 Urea Very high
For the statistical analysis, the Statistical Package for the Social
Sciences (SPSS) program was used. The variance analysis of one
factor and the Tukey test were performed, which were extracted
from ArcGIS through an Excel table that generated the maximum
and minimum limits of each polygonal division of each treatment.
Results and discussion
Capture of the images. High-quality images were captured to
obtain the map with the formulation of the chlorophyll index, the
results of which made it possible to highlight the usefulness of the
sensor for agricultural use. These results are consistent with the
reports of Karydas et al. (2020).
Chlorophyll indices and crop vigor. The colored composition
of the indices calculated in gure 3, showed in green hue, the areas
where the vegetation developed better and the amount of chlorophyll
was high; yellows and oranges indicated less vigor, while reddish
tones represented the soil without vegetation, in the intercrop areas.
In gure 3 it was observed that the plots with maximum vigor of
the cotton crop, corresponded to the treatments T15 and T16 (urea
in doses of 150 and 200 N kg.ha
-1
), as well as the plants treated
with pine nut cake (T10, T11, T12) at doses of 100, 150 and 200 N
kg.ha
-1
and poultry manure at doses of 150 and 200 N kg.ha
-
Simple red border ratio index. The results obtained for
the simple red edge relationship index indicated that there are
signicant differences in the spectral response of the crop between
some treatments (table 4).
As can be seen in able 4, the spectral response of the cotton
crop, according to the simple red edge relationship index, does not
show signicant differences between the different concentrations
of bovine manure. Regarding hen manure, differences were only
observed in the high dose (200 N kg.ha
-1
). There were also no
differences between bovine manure and the low dose of pine nut
cake (50 N kg.ha
-1
), except for the remaining doses of pine nut
cake (100, 150 and 200 N kg.ha
-1
), and the urea treatments in their
different doses.
Figure 3. Schematization of the different chlorophyll indices applied.
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Espinoza and Pacheco. Rev. Fac. Agron. (LUZ). 2022, 39(1): e223919
5-7 |
The hen manure treatments only showed differences among
themselves in the low (50 N kg.ha
-1
) and very high (200 N kg.ha
-1
)
doses. Likewise, hen manure maintains signicant differences only
with the high and very high doses of urea (150 and 200 N kg.ha
-1
).
On the other hand, the high and very high doses of hen manure
showed signicant differences with the pine nut cake and urea
treatments. While the high and very high doses of urea (150 and 200
N kg.ha
-1
), showed signicant differences with bovine manure and
hen manure.
The simple red proportion index can be seen in table 5, where the
differences are not signicant, nding the greatest differences in the
bovine manure treatments at their lowest dose (50 N kg.ha
-1
), with
respect to hen manure treatments at very high doses (200 N kg.ha
-1
), pine
nut cake at doses of 150 and 200 N kg.ha
-1
and urea at a medium
dose (150 N kg.ha
-1
). It was possible to observe how urea, in doses
of 150 N kg.ha
-1
, presented signicant differences, only with the
hen manure dose of 200 N kg.ha
-1
. In relation to the source of urea,
differences were identied between doses 50 and 150 N kg.ha
-1
.
The results of the RARSC reectance spectra ratio index, shown
in table 6, showed that there are no signicant differences between
the bovine manure and hen manure treatments, except in the dose of
50 N kg.ha
-1
. According to the studies carried out by Xu et al. (2019),
the RARSC reectance spectra ratio index contrast representation is
optimal for the application of chlorophyll estimation.
Table 4. Signicance level of the spectral response according to the “simple red edge relationship” index obtained in cotton plants treated
with different nitrogen sources.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 B B B B B B A B A A A A A A A
2 B B B B B B A B A A A B B A A
3 B B B B B B A B B A A B B A B
4 B B B B B B A B A A A B B A B
5 B B B B B B A B B A B B B A B
6 B B B B B B A B A A A B A A A
7 B B B B B B A B A A A B B A B
8 A A A A A
A A A B B B B B B B
9 B B B B B B B A A A A B B A B
10 A A B A B A A B A A B B B B B
11 A A A A A A A B A B B B B B B
12 A A A A B A A B A B B B B B B
13 A B B B B B B B B B B B B A B
14 A B B B B A B B B B B B B B B
15 A A A A A A A B A B B B A B B
16 A A B B B A B B B B B
B B B B
A: Signicant difference. B: There is no signicant difference.
Table 5. Signicance level of the spectral response according to the index of “simple red proportion” obtained in cotton plants treated
with different nitrogen sources.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1
B B B B B B B A B B A A B B A B
2
B B B B B B B B B B B B B B A B
3
B B B B B B B B B B B B B B B B
4
B B B B B B B B B B B B B B B B
5
B B B B B B B B B B B B B B B B
6
B B B B B B B B B B B B B B B B
7
B B B B B B B A B B B B B B A
B
8
A B B B B B A B B B B B B B B B
9
B B B B B B B B B B B B B B B B
10
B B B B B B B B B B B B B B B B
11
A B B B B B B B B B B B B B B B
12
A B B B B B B B B B B B B B B B
13
B B B B B B B B B B B B B B A B
14
B B B B B B B B B B B B B B B B
15
A A B B B B A B B B B B A
B B B
16
B B B B B B B B B B B B B B B B
A: Signicant difference. B: There is no signicant difference.
This scientic publication in digital format is a continuation of the Printed Review: Legal Deposit pp 196802ZU42, ISSN 0378-7818.
Rev. Fac. Agron. (LUZ). 2022, 39(1): e223919. January - March. ISSN 2477-9407.
6-7 |
Table 6. Signicance level of the spectral response according to the “RARSC reectance spectra ratio analysis” index, obtained in cotton
plants treated with different nitrogen sources.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1
B B B B B B B A B A A A A A A A
2
B B B B B B B B B B B B B B A A
3
B B B B B B B A B B B B B B A A
4
B B B B B B B A B B B A B B A A
5
B B B B B B B B B B B B B B A A
6
B B B B B B B A B A A A A A A A
7
B B B B B B B A B A A A A A A
A
8
A B A A B A A B A B B B B B B B
9
B B B B B B B A B B B A B B A A
10
A B B B B A A B B B B B B B A B
11
A B B B B A A B B B B B B B B B
12
A B B A B A A B A B B B B B B B
13
A B B B B A A B B B B B B B A B
14
A B B B B A A B B B B B B B A B
15
A A A A A A A B A A B B A
A B B
16
A A A A A A A B A B B B B B B B
A: Signicant difference. B: There is no signicant difference.
The results obtained for the hen manure cake in medium doses (100
and 150 N kg.ha
-1
), showed differences with the treatments of pinion
cake (100, 150 and 200 N kg.ha
-1
) and urea in all its dose. Likewise,
a singularity was observed in the same treatments, especially in the
urea source, with which a spectral response was observed where the
low doses (50 and 100 N kg.ha
-1
) maintain signicant differences with
the highest doses. (200 N kg.ha
-1
). According to similar investigations
carried out by Zhou et al. (2017), the RARSC vegetation index yields
fairly robust results for this categorization and the estimation of
the chlorophyll content of crops.
For its part, the spectral response of the normalized difference
red edge index “NDRE” (table 7), reected that the concentration
of bovine manure in its lowest dose presented signicant
differences with all treatments in their different doses, such as hen
manure (50 and 200 N kg.ha
-1
), pine nut cake (100, 150 and 200 N
kg.ha
-1
) and urea.
Table 7. Signicance level of the spectral response according to the “NDRE normalized difference red edge index”, obtained in cotton
plants treated with different nitrogen sources.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 B A B B A B B A B A A A A A A A
2 B B B B B B B B B A A A B A A A
3 A B B B B B B B A B B B B B A A
4 B B B B B B B B B A A A A A A A
5 A B B B B B B B B B B A B B A A
6 B B B B B B B B B A A A A A A A
7 B B B B B B B B B A A A B A A
A
8 A B B B B B B B A B B B B B A A
9 B B A B B B B A B A A A A A A A
10 A A B A B A A B A B B B B B A B
11 A A B A B A A B A B B B B B A B
12 A A B A A A A B A B B B B B B B
13 A B B A B A B B A B B B B B A B
14 A A B A B A A B A B B B B B A B
15 A A A A A A A A A A A B A
A B B
16 A A A A A A A A A B B B B B B B
A: Signicant difference. B: There is no signicant difference.
7-7 |
The treatments with hen manure source presented signicant
differences in their medium and high doses (150 and 200 N kg.ha
-1
),
with respect to the pine nut cake and urea treatments for all doses.
On the other hand, pine nut cake showed signicant differences with
bovine manure (50, 100 and 200 N kg.ha
-1
) and poultry manure in the
medium doses (100 and 150 N kg.ha
-1
).
Finally, as shown in table 7, the urea treatment with medium dose
(150 N kg.ha
-1
), was the one that presented a signicant difference
between treatments, except with pine nut cake at its highest dose
(200 N kg.ha
-1
), so it was considered the source that favored the
development of the cotton crop, based on the observed spectral
response.
Reectance levels. The greater vigor of the crop, in view of the
chlorophyll indices evaluated, does not correspond to the maximum
concentration of urea (200 N kg.ha
-1
) applied in this investigation
(gure 4). Although it is true that there are no gures on recommended
doses, many producers exceed this amount, which implies an excess
of product that unnecessarily increases production costs, in addition
to contributing to environmental problems of contamination and
Figure 4. Reectance levels of the cotton crop according to the
applied treatment.
Likewise, it was observed that the plants treated with pine nut
cake and hen manure show vigor and can become substitutes for urea.
The plots with bovine manure presented the lowest vigor in the crop.
Conclusions
The unmanned aerial vehicle showed great efciency for the
application of the procedures used, forming a fundamental part in the
application of technology in agriculture and production, thanks to its
easy handling and the large amount of information it can generate.
The analyzed indices were able to visually show the differences in
the vigor of the crop, depending on the various nitrogen fertilization
treatments. Therefore, with the application of this technology, the
application of fertilizers can be optimized, by selecting the best
nitrogen source for the study conditions.
The GIS tool proved to be very useful in differentiating the areas
of the crop with greater or lesser development of the plants based
on the chlorophyll index, thus being able to take advantage of the
information obtained to cover the needs of the areas with nutritional
deciency.
The application of the chlorophyll indices made it possible to
determine the most effective nitrogenous sources in plants, with urea
at a dose of 150 N kg.ha
-1
being the source with the best spectral
response for the four calculated indices.
The results of the research allow the recommendation of doses
and nitrogenous sources that could imply improvements in crop
production in economic and environmental terms in the study area.
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