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VEGETATION INDICES FROM ACTIVE CROP CANOPY SENSOR AND THEIR POTENTIAL INTERFERENCE FACTORS ON SUGARCANE

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Among the inputs usually used in sugarcane production the nitrogen (N) is the most important due to its highly variable response. In sugarcane the use of canopy sensors to N management is a possibility as it is done in other crops. However, because the sugarcane is different from the crop that the sensors were initially developed, it is necessary to evaluate the efficiency of different vegetation indices (VI) as estimators of N nutrition, as well as to identify potential interference factors. Therefore, studies with canopy sensors have been developed in Brazil for sugarcane since 2007. The objective of this study was to evaluate the performance of seven VIs for sensing N status and to evaluate the effect of daytime and the conditions of substrate and wetness on leaves. It was not possible to identify a behavior pattern of VIs on measurements throughout the day. The index MCARI/OSAVI was not affected by the substrate and by the water on the leaves and it was able to identify the levels of N, although it had weak relationship with the chlorophyll content in leaves. NDVI, NDRE and Yara ALS were efficient in identifying the N rates, showing high correlation with chlorophyll content, but susceptible to interference factors.
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VEGETATION INDICES FROM ACTIVE CROP CANOPY SENSOR AND
THEIR POTENTIAL INTERFERENCE FACTORS ON SUGARCANE
L. Taubinger, L.R. Amaral, J.P. Molin
Biosystems Engineering Department, "Luiz de Queiroz" College of Agriculture
University of São Paulo
Piracicaba, São Paulo, Brazil
ABSTRACT
Among the inputs usually used in sugarcane production the nitrogen (N) is the
most important due to its highly variable response. In sugarcane the use of canopy
sensors to N management is a possibility as it is done in other crops. However,
because the sugarcane is different from the crop that the sensors were initially
developed, it is necessary to evaluate the efficiency of different vegetation indices
(VI) as estimators of N nutrition, as well as to identify potential interference
factors. Therefore, studies with canopy sensors have been developed in Brazil for
sugarcane since 2007. The objective of this study was to evaluate the performance
of seven VIs for sensing N status and to evaluate the effect of daytime and the
conditions of substrate and wetness on leaves. It was not possible to identify a
behavior pattern of VIs on measurements throughout the day. The index
MCARI/OSAVI was not affected by the substrate and by the water on the leaves
and it was able to identify the levels of N, although it had weak relationship with
the chlorophyll content in leaves. NDVI, NDRE and Yara ALS were efficient in
identifying the N rates, showing high correlation with chlorophyll content, but
susceptible to interference factors.
Keywords: Proximal sensing, nitrogen application, multispectral sensor, sensor
interferences
INTRODUCTION
Sugarcane (Saccharum spp.) is the most important crop in sugar and ethanol
production in tropical and subtropical regions, accounting for approximately 80%
of world sugar production and about 35% of global ethanol production (FAO,
2011). Brazil is the main producer, within 570 million tons produced in 7.1
million hectares (Agrianual, 2010), accounting for more than a third of world
production (FAO, 2011), with great economic, social and environmental
importance. The application of more efficient processes, which increase yield and
reduce production costs, mainly by lowering inputs use, is crucial for the
development of the sector.
Under Brazilian conditions generally soil analysis for nitrogen (N)
recommendation is not used. The recommendations are made based on soil type,
variety and age of the field (plant cane or stubble cane – number of ratoons),
without taking into account the availability of N in the soil and its spatial
variability. Thus, the use of canopy sensors is an alternative to the traditional
recommendation of N (Amaral and Molin, 2011).
The use of canopy sensors has been effective in N fertilization in different
crops (Raun et al., 2002; Kitchen et al., 2010; Vellidis et al., 2011). However, in
sugarcane, this technique is still a challenge (Molin et al, 2010; Amaral and
Molin, 2011; Lofton et al, 2012).
Bausch and Brodahl (2012) indicate that several vegetation indices (VI) have
been evaluated and developed to enable the N management during the growing
season in different crops. Among the many factors that affect the reflectance of
crops and consequently the vegetation indices are the stress, climate, soil and
plant factors.
Eitel et al. (2008), working with wheat, concluded that simple indices such as
NDVI and CI are influenced by other factors such as the amount of biomass and
the influence of the substrate, while the compost index MCARI/MTVI2, which
takes into account the reflectance of specific wavelength bands of blue, green, red
and near infrared, better correlates with the N status.
For corn, Wu et al. (2008) found the same result using the compost index
MCARI/OSAVI, which was more appropriate for estimating the chlorophyll
content in the leaves.
Thus it is necessary to conduct studies to understand the behavior of vegetation
indices that better express the nutritional status of sugarcane and are less
susceptible to the influence of other variables. Thus, the objective of this study
was to verify the influence of different substrates, the effect of wetness on leaves
during the measurements and the variation throughout the day in different
vegetation indices calculated from canopy sensor, as well as to assess the
effectiveness from the VIs in identifying N rates applied to the sugarcane.
MATERIALS AND METHODS
The study consisted of experiments in greenhouse and in field with sugarcane
(Saccharum spp.). In the greenhouse (daytime experiment) the effect of
measurements made with canopy sensor at different moments of the day was
analyzed. In the field the effects of N rates, substrate types and wetness on leaves
in the VIs obtained with canopy sensor were analyzed.
The measurements were performed with the canopy sensor (CropCircle, Model
ACS-470, Holland Scientific, NE, USA), which provides the reflectance at
wavelengths of 450, 550, 650, 670, 730 and 760 nm, by exchanging optical filters
and sensor calibration and the vegetation indices used were obtained from those
wavebands (Table 1). Daytime experiment
The experiment was conducted in a greenhouse of the Biosystems Engineering
Department, ESALQ/USP, Piracicaba, SP, Brazil (22° 42' S - 47° 37' W). Three
varieties (CTC 9, SP 90-3414 and RB 855156) were planted in January 2009 in
pots of 0.5 m3 with medium textured soil. Pots received water via drip irrigation,
keeping the soil moisture at field capacity.
To check the influence of the moment of the day on the different VIs, due to
possible plants physiological changes along the day, the measurements were taken
every two hours, between 6:00 h and 20:00 h. Three measurements were obtained
(around 100 values per reading) at different points in each pot in each daytime
reading.
At the same time readings with portable chlorophyll meter (SPAD-502, Konica
Minolta Sensing Inc., Sakai, Osaka, Japan) also performed. Two diagnostic leaves
were adopted for comparison purposes, the TVD (top visual diulep - leaf +1) and
the other leaf was two expanded leaves below (leaf +3 - oldest leaf). A
measurement in the middle of the leaf blades on five distinct leaves was realized
per pot.
on all evaluations the reflectance measurements were realized adapting the
sensor to a support leg, so all the measurements captured the reflectance from the
same plant site. Similarly, the leaves measured with the chlorophyll meter were
marked, performing the readings always on the same leaves.
Due to lack of true replicates (one pot for each variety), even working with
readings in different places in the pots, it is a concern that using analysis of
variance and mean comparison tests would be inconsistent. Thus, the mean values
for each daytime was calculated and the confidence interval for the mean (95%)
was estimated and the graphs were plotted for visual analysis.
Table 1. Vegetation indices used with indication of their respective authors;
due to the available optical filters, some changes were made in this work:
between wavelength 760 and 800 nm was used 760 nm, between 550 and 590
nm was used 550 nm and between 700 and 730 nm was used 730 nm.
Vegetation Index Equation Reference
NDVI (R760 - R670)/(R760 + R670) Rouse et al. (1974)
CI (R760/R590) – 1 Gitelson et al. (2005)
GNDVI ( R780-R550 )/( R780+R550 )
Gitelson
and
Merzlyak (1996)
Yara ALS 100(ln(R760) – ln (R730)) Jasper et al. (2009)
NDRE (R760 - R730)/( R760 + R730) Barnes et al. (2000)
MCARI/MTVI2 MCARI [(R700–R670) – 0.2(R700–R550)](R700/R670) Eitel et al. (2008)
MTVI2
{1.5[1.2(R
800
-
R
550
)
2.5(R
670
-
R
550
)]} /
{√[(2R
800
+1)2 – (6R
800
-5√R
670
) – 0.5]}
MCARI/OSAVI MCARI [(R700–R670) – 0.2(R700–R550)](R700/R670) Wu et al. (2008)
OSAVI (1+0.16)(R800-R670) / (R800+R670+0.16)
Field experiments
The objectives of the field experiments were to identify potential factors that
affect the different VIs obtained by the canopy sensor. The experiments were
divided into: effects of N rates, influence of the substrate and from the wetness of
the leaves.
Effect of nitrogen rates
Canopy sensors have been designed for identifying N nutrition of crops like
wheat and corn, which have leaf architecture and development behavior different
from sugarcane. Because of that it is necessary to examine if some VI could be
more efficient in identifying sugarcane N response.
In order to reach that, an experiment with N rates conducted by São Paulo
Agency of Agribusiness Technology (APTA) and Agronomic Institute of
Campinas (IAC), Piracicaba-SP, Brazil (22°41´ S – 47°38´ W) was evaluated. The
plots consisted of five 10 m long sugarcane rows, with four replications in
randomized blocks. Treatments were the application of four N rates (0, 50, 100
and 150 kg N ha-1), and the variety grown was IAC 87-3396. The evaluation was
made in the second ratoon (first stubble) of the crop with the application of such
treatments for two consecutive growing seasons.
The assessment with portable chlorophyll meter (SPAD-502) was also done in
the same time following the procedure described previously (Daytime
experiment) but with 20 readings per plot.
The evaluation occurred when the plants were 0.5 m average stem height
(Amaral and Molin, 2011; Portz et al., 2012). The sensor was maintained at an
average distance of 0.8 m from the canopy, driven manually with a collection
frequency of 10 Hz. The data were analyzed by analysis of variance and when
significant, comparison means test was proceed (Scott-Knott at 5%), regression
analysis and linear correlation by SISVAR statistical software (Ferreira, 2011).
Substrate influence
The area measured by the sensors is variable in function of the height and
biomass of the crop, so not always the emitted light beam hits only the plant
canopy, also capturing reflectance from soil and residue, which could cause noise
in the measured values. Seeking to verify this influence, readings were taken on
different substrates in a 10 m long sugarcane row of variety CTC2 in the fourth
ratoon (third stubble), with average stem height of 0.5 m.
The substrate conditions were: sugarcane straw originated from mechanized
harvesting deposited on the ground (14 Mg ha-1); manual removal of straw,
exposing the clay soil (dark red); deposition of sand on the soil surface, to
simulate the surface reflectance of a sandy soil. Six dynamic measurements were
realized (six replications) on each substrate condition, with the sensor kept at an
average distance of 0.8 m of the canopy, driven manually with a sampling
frequency of 10 Hz.
The data were submitted to analysis of variance and comparison means test
(Scott-Knott at 5%) by SISVAR statistical software (Ferreira, 2011).
Influence of water on the leaves
Sugarcane producers need to fertilize large areas, working 24 hours a day.
Thus, even under conditions of light rainfall or in the presence of dew, the
operation cannot be interrupted. Therefore, we must examine if there is influence
of the wetness on leaves in the measurements with canopy sensors, and if there
are some VI that reduces this effect.
We used a backpack sprayer equipped with a large drop diameter nozzle
generator to simulate rainfall (32.4 mm h-1). Readings with the sensor were
performed before (dry), during (rain) and after (dew) the rainfall simulation (Fig.
1). For each condition four static measurements were taken (about 600 values) in
four distinct spots of a field planted with the variety CTC2 in the fourth ratoon.
The data were submitted to analysis of variance and comparison means test
(Scott-Knott at 5%) by SISVAR statistical software (Ferreira, 2011).
Fig. 1. Collecting data with the sensor before (A) and during (B) the rainfall
simulation; wet leaves after the rain simulation (C).
RESULTS AND DISCUSSION
Daytime experiment
In the measurements throughout the day (Fig. 2) it was not possible to find a
behavior pattern that could be explained by plant physiology and/or remote
sensing. If the behavior was analyzed for each variety, a behavior pattern as
function of daytime could be inferred. However, analyzing the behavior of more
than one variety this false assumption was avoided.
About the VIs, the large variation observed between daytime must have
occurred mainly by small, however, the existing change in position and angle of
the sensor in relation to the leaves of plants in each measurement. For the SPAD
values the wide variation occurred due to the large variability in the readings
taken on the same leaf.
It could be observed that when the wavelengths were the same (eg. CI and
GNDVI or NDRE and Yara ALS) the response was very similar. Moreover, using
the same equation with different wavelengths, the behavior was different (eg.
NDVI, GNDVI and NDRE). These findings concerns when considering the
possibility of adapting the original VIs in function of optical filters available in a
Fig. 2. Vegetation indices and SPAD values obtained throughout the day
(from 6:00 to 20:00 h) and their confidence intervals in the three sugarcane
varieties studied.
0.36
0.4
0.44
0.48
0.52
0.56
0.6
GNDVI
0.5
0.53
0.56
0.59
0.62
0.65
0.68
NDVI
1
1.3
1.6
1.9
2.2
2.5
2.8
3.1
CI
0.25
0.29
0.33
0.37
0.41
0.45
0.49
0.53
MCARI/MTVI2
0.13
0.17
0.21
0.25
0.29
0.33
0.37
MCARI/OSAVI
55
59
63
67
71
75
79
6 8 10 12 14 16 18 20
Yara ALS
0.27
0.286
0.302
0.318
0.334
0.35
0.366
6 8 10 12 14 16 18 20
NDRE
27
29
31
33
35
37
39
41
43
45
6 8 10 12 14 16 18 20
SPAD + 1
29
32
35
38
41
44
47
50
6 8 10 12 14 16 18 20
SPAD + 3
Daytime
CTC 9
SP 90-3414
RB 855156
given study. In this kind of study the results must take into account the
wavelengths actually used, not only the name of the studied VI. Such
identification was efficiently realized, for example, in the work by Wu et al.
(2008) and Shiratsuchi et al. (2010), who differentiated the VIs in their initial
configurations from VIs that had some adaptation on spectral bands, like NDVIred
and NDVIred-edge.
In the conditions of this study it was not possible to identify a standard
behavior of VIs measured throughout the day. However it is not possible to say
that daytime interference does not exist. In conditions of reduced water
availability the plants tend to reduce their metabolism during the hottest hours of
the day, as also rolling the leaves to reduce water loss by transpiration (Lisson et
al., 2005). Because of this change in leaf architecture the reflectance may be
changed. This mechanism can vary considerably between varieties and can be
correlated with water stress tolerance (Inman-Bamber, 2004), hence the
importance of studying different varieties. Thus, more studies should be
conducted to really dispose this interference factor.
Field experiments
Effect of nitrogen rates
The VIs were distinct in their capability in identifying the N rates (Table 2).
CI, GNDVI and MCARI/MTVI2 were inefficient in capturing the different N
rates (p> 0.05). Eitel et al. (2008) stated that the MCARI/MTVI2 was a good
estimator of chlorophyll and N leaf for wheat. However, in the conditions of this
study the VI was not able to differentiate N rates.
Moreover, Wu et al. (2008), also working with wheat and some VIs, found that
MCARI/OSAVI was the best one for determination of chlorophyll in the leaves
by satellite images. This VI was efficient in identifying the N rates in the present
study, similar to the NDVI, Yara ALS and NDRE.
It was possible to observe high difference between the treatments with N and
no N treatment, explained by the low response to N that occurs in many Brazilian
situations (Cantarella et al., 2007).
Emphasis should be given to the CI, where previous studies have found great
similarity with NDVI, both calculated from the wavelengths of the 590 nm
(amber) and 880 nm (Solari et al., 2008; Shiratsuchi et al., 2010; Amaral and
Molin, 2011). However, when working with wavelengths in the visible region
nearest to the originals, respectively 560 and 670 nm for CI (Gitelson et al., 2003)
and NDVI (Rouse et al., 1974) this high similarity did not happen, corroborating
what was observed also in the daytime experiment. Thus, the setting to the CI
used in this study (green) made it unable to identify plant N nutrition, as also was
highly variable with CV higher than 13%.
SPAD measurements for both the leaf +1 and leaf +3 were able to identify
difference between N rates better than the VIs from the canopy sensor (Table 2).
Analyzing the relationship of VIs with leaf chlorophyll content (SPAD values) we
observed that MCARI/OSAVI had a reduced effectiveness in relation to the others
VIs (Table 3). These data show that the correct estimation of sugarcane N demand
based on canopy sensors is still a challenge.
Table 2. Mean values for the vegetation indices and SPAD measured in the
leaves +1 and +3 in the different N rates; analysis of variance (ANOVA),
linear and quadratic regression (p<F)
N rate
(kg ha-1)
Vegetation indices SPAD
NDVI CI GNDVI MCARI/
MTVI2 MCARI/
OSAVI Yara ALS NDRE Leaf + 1 Leaf + 3
0 0.475 a 1.783 0.462 0.342 0.194 a 53.02 a 0.259 a 45.278 a 45.015 a
50 0.557 b 1.859 0.480 0.362 0.239 b 62.38 b 0.302 b 46.173 a 47.780 b
100 0.584 b 2.051 0.503 0.372 0.252 b 65.84 b 0.318 b 47.058 b 49.688 c
150 0.599 b 2.235 0.525 0.370 0.251 b 68.56 b 0.330 b 48.508 c 50.038 c
ANOVA 0.008 0.130 0.179 0.229 0.038 0.004 0.003 0.001 0.001
Linear
regression 0.002 - - - 0.012 0.001 0.001 < 0.001 < 0.001
Quadratic
regression 0.127 - - - 0.116 0.160 0.150 0.436 0.090
CV (%) 7.25 13.07 7.75 5.76 11.28 6.95 6.56 1.45 2.65
(1) Different letters indicate difference between the means of treatments by Scott-Knott test at 5%
There is not a local consensus yet about the leaves to be taken for
evaluation. In this study, the leaf +3 showed a better adjustment of the quadratic
function as well as lower RMSE and higher R2, which may be an indication that
this leaf is most suitable for estimation of the sugarcane nutrition status. Because
this element is movable in the plant, older leaves must be able to identify the lack
of N earlier, so the leaf +3 may be preferred. The relationship between VIs and
SPAD was better than that observed by Eitel et al. (2008) in wheat. However, it is
necessary to develop other studies to prove the effectiveness of the VIs in
estimation of N status and the best leaf to be sampled in the sugarcane.
Substrate influence
It was observed variable influence of substrates in the VIs (Table 4). The
results corroborate Wu et al. (2008) and Eitel et al. (2008) to whom the indices
MCARI/OSAVI and MCARI/MTVI2 were not influenced by the substrate. Also,
the NDRE was the VI that presented the best results, evidenced by low values
changes observed between treatments. The index Yara ALS was also able to
minimize the influence of substrate. CI again showed inconsistence of data and a
high coefficient of variation (CV). Greater variation in the CV to amber CI was
also observed by Amaral and Molin (2011) in comparison with amber NDVI.
However, even showing high CV, this VI configuration was able to identify N
rates applied.
NDVI and GNDVI were susceptible to substrate interference, probably due to
the strong influence of wavelengths in the visible region of these indices. Thus,
changing the color of the substrate, also changes the reflectance values from
canopy/substrate and consequently the VI values. Huete (1989) reports that the
ground contributions of vegetation spectral response vary within the quantity
exposed, surface condition and their intrinsic properties, such as the
mineralogical, organic and water absorption characteristics.
Table 3. The RMSE and R2 for ANOVA significant vegetative indices used
as regression estimators of N rates and SPAD values in leaf +1 and +3
N rate SPAD +1 SPAD +3
RMSE R2 RMSE R2 RMSE R2
NDVI 46.2 0.346 2.045 0.262 2.331 0.339
MCARI/ OSAVI 52.2 0.163 2.365 0.013 2.780 0.060
Yara ALS 48.0 0.293 1.984 0.305 2.307 0.353
NDRE 47.4 0.311 1.965 0.318 2.273 0.372
SPAD +1 48.5 0.277 - - - -
SPAD +3 38.3 0.550 - - - -
Grohs et al. (2009), working with red NDVI identified that there was
interference when substrate conditions were corn or soybean straw. This shows
that changes in substrate must be carefully observed by the users of canopy sensor
technology for nitrogen fertilization, especially in crop stages with significant
visible substrate.
Influence of water on the leaves
Variable influence from water conditions on the leaves was observed within
the different VIs (Table 5). This may be a concern in field conditions, because the
operation of fertilizer application has a short period for implementation and must
not be interrupted. Thus, N application based on canopy sensors can be
susceptible to errors in dosage when rain events occur, both during the rain or
while the leaves are not completely dry.
Table 4. Vegetation indices values observed in different substrate conditions:
clay soil exposed, straw and sand on the surface
NDVI CI GNDVI MCARI/MTVI2 MCARI/OSAVI Yara ALS NDRE
Straw 0.633 a 2.46 a 0.549 b 0.444 a 0.329 a 68.61 a 0.330 a
Sand 0.643 a 1.79 a 0.471 a 0.450 a 0.358 a 67.94 a 0.327 a
Clay soil 0.673 b 2.67 a 0.558 b 0.458 a 0.348 a 73.95 a 0.353 a
CV % 3.000 27.80 9.890 7.820 9.520 9.84 8.660
standard error 0.008 0.26 0.021 0.014 0.013 2.82 0.012
(1) Different letters indicate difference between the means of treatments by Scott-Knott test at 5%
NDVI, Yara ALS and NDRE were influenced by treatments, indicating that
water conditions at the time of evaluation with canopy sensors is a source of error
and should be carefully managed. For the remaining VIs no significant
interferences were observed, probably due to the high CV, which represent an
inconsistency of the data.
This influence of water on the canopy reflectance of the plants was also
observed by Madeira et al. (2001), that working with grasslands found that the
dew and the presence of water on the plant canopy increased reflectance in the
visible (VIS) and decreased in the mid-infrared (MIR) and near infrared (NIR).
Table 5. Vegetation indices for rainfall conditions, dew and dry
NDVI CI GNDVI MCARI/MTVI2 MCARI/OSAVI
Yara ALS NDRE
Rain 0.554 a 1.02 a 0.330 a 0.458 a 0.439 a 55.98 a 0,272 a
Dew 0.580 b 1.00 a 0.303 a 0.445 a 0.435 a 58.17 b 0.283 b
Dry 0.613 c 1.41 a 0.379 a 0.481 a 0.437 a 61.12 c 0.296 c
CV % 2.240 31.79 24.340 12.960 11.570 2.07 1.920
Standard error 0.007 0.18 0.041 0.030 0.025 0.61 0.003
(1) Different letters indicate difference between the means of treatments by Scott-Knott test at 5%
Moreover a drizzle decreased the reflectance in the near infrared (NIR). These
changes influence the values obtained by the VIs and could be a problem to the
user of this technology.
CONCLUSIONS
In the conditions of this study it was not possible to identify behavior patterns
of VIs in the measurements throughout the day.
Among the studied VIs, MCARI/OSAVI showed interesting results. It did not
show interference of the substrate and the wetness on leaves, and was effective in
identifying the N rates. However, its relationship with leaf chlorophyll content
was low.
NDRE and Yara ALS were similar in all analyzes performed, being efficient in
identifying the N rates, with high correlation to the chlorophyll leaf content, but
they were sensitive to wetness on leaves. The NDVI showed the same
characteristics, but unlike the two previous VIs, it was also sensitive to variations
in substrate.
CI, GNDVI and MCARI/MTVI2 showed variable sensitivity to substrate
conditions and wetness on leaves while they were not efficient in identifying the
N rates, therefore they should not be used for nitrogen status.
ACKNOWLEDGMENTS
We acknowledge the Research and Projects Financing (FINEP) and National
Council for Scientific and Technological Development (CNPq) for financial
support and for providing scientific initiation fellowship to the first author; São
Paulo Research Foundation (FAPESP) for providing the doctorate fellowship to
the second author; São Martinho’s Mill team, Agronomic Institute of Campinas
(IAC) and Professor Rubens Duarte Coelho (ESALQ/USP) for allowing the use of
his greenhouse facilities and experiments.
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... Ref. [40], when evaluating the response of NDVI and NDRE for soybean cultivar, reported that NDRE did not show evidence of saturation for proximal sensors. This result was supported by hypotheses that VI which contains the red-edge band, which has been commonly reported in studies with highly dense vegetation [40][41][42]. ...
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The monitoring and determination of peanut maturity are fundamental to reducing losses during digging operation. However, the methods currently used are laborious and subjective. To solve this problem, we developed models to access peanut maturity using images from unmanned aerial vehicles (UAV) and satellites. We evaluated an area of approximately 8 hectares in which a regular grid of 30 points was determined with weekly evaluations starting at 90 days after sowing. Two Artificial Neural Networking (ANN) were used with Radial Basis Function (RBF) and Multilayer Perceptron (MLP) to predict the Peanut Maturity Index (PMI) with the spectral bands available from each sensor. Several vegetation indices were used as input to the ANN, with the data being split 80/20 for training and validation, respectively. The vegetation index, Normalized Difference Red Edge Index (NDRE), was the most precise coefficient of determination (R2 = 0.88) and accurate mean absolute error (MAE = 0.06) for estimating PMI, regardless of the type of ANN used. The satellite with Normalized Difference Vegetation Index (NDVI) could also determine PMI with better accuracy (MAE = 0.05) than the NDRE. The performance evaluation indicates that the RBF and MLP networks are similar in predicting peanut maturity. We concluded that satellite and UAV images can predict the maturity index with good accuracy and precision.
... As the NDVIRE index is readily obtainable with the ACS-430 model as described, the results from the former studies are comparable. In general, previous work has demonstrated NDVIRE to be an advantage in assessing plant N status, as it has been shown to exhibit a higher sensitivity to differentiating N rates than the traditional NDVIR measurement (Amaral et al., 2015;Erdle et al., 2011;Taubinger et al., 2012). The reason for this may be the decreased susceptibility of red-edge wavebands to soil background noise, which can result in sensor saturation in the visible waveband region (Baret and Guyot, 1991). ...
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The industry standard method to determine grapevine canopy nitrogen (N) status is through leaf and petiole tissue analysis. The accepted method is destructive, expensive and the results often require technical assistance to interpret. A rapid and simpler method to predict vine canopy N status would be beneficial to the viticultural industry. The utilisation of proximal sensors (GreenSeeker, Crop Circle ACS-430 and SPAD-502) and near-infrared spectroscopy (NIRS) to predict vine canopy N content was explored in Vitis vinifera Pinot Noir and Chardonnay cultivars in Southern Tasmania, Australia. The measurements were correlated with leaf N content (%) obtained from Dumas Combustion via elemental analysis at three sampling dates (January, February and March) during the 2017-18 growing season. The GreenSeeker demonstrated the greatest sensor potential to predict vine canopy nitrogen content (r2 = 0.92). However, its predictability potential was both cultivar and sampling time dependent, as found with the Crop Circle ACS-430 and SPAD-502 meter. Alternatively, NIRS strongly predicted vine canopy N content independent of sampling time and cultivar (r2 = 0.94, RMSECV = 0.071). This study demonstrates that lab-based NIRS has the strongest potential to be utilised as tool to predict vine canopy N status. Further research is required to assess its suitability on fresh vine leaf samples in the field to provide industry with a non-destructive alternative.
... As the NDVIRE index is readily obtainable with the ACS-430 model as described, the results from the former studies are comparable. In general, previous work has demonstrated NDVIRE to be an advantage in assessing plant N status, as it has been shown to exhibit a higher sensitivity to differentiating N rates than the traditional NDVIR measurement (Amaral et al., 2015;Erdle et al., 2011;Taubinger et al., 2012). The reason for this may be the decreased susceptibility of red-edge wavebands to soil background noise, which can result in sensor saturation in the visible waveband region (Baret and Guyot, 1991). ...
... Regarding the vegetation indices, NDRE presented the highest correlations when compared to the other indices for the parameters under analysis. On sugarcane, Amaral et al. (2015a) and Taubinger et al. (2012) found that NDRE had less influence on the plant canopy and was more efficient at predicting biomass when compared to NDVI. ...
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Active optical sensors have been widely used for the spatial and temporal monitoring of peanut culture because they are accurate, non-destructive methods for rapidly obtaining data. The objective of this study was to determine the optimal stage of crop growth for collecting sensor readings based on correlations between quality indicators. In addition, we compared vegetation indices (Normalized Difference Vegetation Index [NDVI], Normalized Difference Red-Edge Index, [NDRE], and Inverse Ratio Vegetation Index, [IRVI]) by monitoring temporal variability in the peanut crop in order to determine which of them obtained the best reading quality throughout the process. The experiment was performed on the 2016/17 crop in the agricultural area of the municipality of Dumont in the state of São Paulo, Brazil. The experimental design was based on the basic assumptions of statistical quality control and contained 63 sample points in a 30 × 30 m grid. The parameters were evaluated at 30, 45, 60, 75, and 119 days after sowing (DAS) using proximal sensing with GreenSeeker and OptRX sensors. We found that 45 and 60 DAS were the optimal times for monitoring peanut crop variability. For spatiotemporal monitoring of the culture with control charts, NDRE showed the best readings throughout the process when compared to NDVI and IRVI.
... Moreover, the higher NDVI data dispersion obtained from GreenSeeker and Crop Circle ACS-430 NDVI might be due to the fact that these sensors function using wavebands in the visible region of the spectrum, thereby increasing the reflectance ''noise'' due to soil background reflectance and the variety of colors (leaves and stalks). Taubinger et al. (2012), testing for factors interfering with canopy sensor readings in sugarcane, found that the vegetation indices based on visible wavebands were more susceptible to the influence of soil background than those based on red-edge wavebands. ...
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Optimized nitrogen fertilization of sugarcane is still a challenge. Crop canopy reflectance sensors can potentially help in improving N management. However, there is no information available in the literature about canopy sensor efficiency as well as about vegetation indices that are suitable for sugarcane production. Therefore, a comparison was undertaken. Two fields were scanned with three canopy sensors (GreenSeeker and CropCircle, models ACS-210 and ACS-430), resulting in five vegetation indices (VI). The resultant spatial data were compared. In order to compare the efficiency of each device, plant samples were taken and analyzed, and then the results were correlated with the sensed data. Both Crop Circle sensors had similar results, regardless of the VI used. Although the GreenSeeker sensor had the lowest capability to identify sugarcane vigor, based on biomass samples, all sensors are able to identify the variability in crop development.
... Canopy reflectance was recorded in the red edge (730 nm) and near infrared (NIR, 760 nm) wavelengths and processed by calculating the normalized difference red edge index [NDRE = (NIR -red edge reflectance)/(NIR + red edge reflectance); Barnes et al., 2000]. The NDRE index was selected because several previous sugarcane studies showed it to be less influenced by canopy and substrate color and it is a more efficient predictor of sugarcane biomass than the normalized difference vegetation index (NDVI), which uses red band reflectance from the same sensor (Amaral et al., 2015;Taubinger et al., 2012). Moreover, the NDRE index has provided more promising results for predicting yield than other vegetation indices derived from Crop Circle ACS-430 bands, such as the red NDVI, canopy chlorophyll content index (CCCI), and the MERIS terrestrial chlorophyll index (MTCI) (unpublished data). ...
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Nitrogen fertilization is challenging for sugarcane (Saccharum spp.) producers due to its complex interaction with the crop and soil. Thus, the main goal of this study was to develop a feasible approach to guide variable-rate N application in sugarcane based on canopy sensor readings. This study was conducted for 5 yr. Several plot and strip N-rate experiments were conducted under a wide range of crop conditions in Brazil and evaluated with the Crop Circle active canopy sensor (Holland Scientific Inc.). Because of variability in crop density and growth development within sugarcane fields, the use of an N-rich reference area to estimate the crop response to N application was compromised. Biomass was the main crop parameter influencing canopy sensor readings, allowing yield estimation because biomass typically results in stalk yield. Thus, canopy sensor readings can efficiently predict relative sugarcane yield when working with data that are normalized to the mean for the field. Hence, an algorithm that takes into account this relationship was established. The concept of this algorithm is to apply higher N fertilization rates where the sugarcane yield potential is higher. Such an approach was determined to be useful to guide N application in sugarcane fields. Nevertheless, field validation is needed to confirm this N management strategy. Besides, more information about sugarcane biomass variability within fields may be required to increase algorithm efficiency. Paper can be accessed in: https://dl.sciencesocieties.org/publications/aj/abstracts/107/4/1513
... Moreover, the higher NDVI data dispersion obtained from GreenSeeker and Crop Circle ACS-430 NDVI might be due to the fact that these sensors function using wavebands in the visible region of the spectrum, thereby increasing the reflectance ''noise'' due to soil background reflectance and the variety of colors (leaves and stalks). Taubinger et al. (2012), testing for factors interfering with canopy sensor readings in sugarcane, found that the vegetation indices based on visible wavebands were more susceptible to the influence of soil background than those based on red-edge wavebands. ...
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Canopy reflectance sensors are useful tools for guiding nitrogen fertilization in crops. However, studies of sugarcane comparing the efficiency of different devices for determining crop parameters are scarce. The objective of this study was to compare the performance of canopy sensors in detecting sugarcane variability. Four nitrogen (N) rate experiments were conducted (plots), along with biomass sampling, chlorophyll meter readings and leaf N concentration determination in another four fields by canopy sensor readings guided samplings. The examined canopy sensors were GreenSeeker and two Crop Circle models (ACS-210 and ACS-430), which allowed the calculation of different normalized difference vegetation index (NDVI) configurations. Neither of the canopy sensors showed a correlation with the obtained chlorophyll meter readings (SPAD) or leaf N content within the fields, while high correlations with above-ground biomass were found, indicating that the plant population and vigor interfered with the canopy sensor readings. The devices showed similar suitability in terms of N rate differentiation and correlations with crop parameters. However, the NDVI calculated from the Crop Circle ACS-430 sensor using a red-edge waveband (NDRE) showed the best results, displaying the greatest range of measured values and the highest sensitivity as a biomass predictor. Regardless of the canopy sensor and wavebands used, all of the analyzed sensors proved to be good tools for identifying the variability of crop development in sugarcane fields.
... Moreover, red-edge CI and MTCI showed less influence of soil background effects. Similarly, Taubinger et al. (2012) found that the red NDVI was more affected by the soil background than was the red-edge NDVI in sugarcane but also found that both were effective at identifying N rates applied to the crop. Kitchen et al. (2010), used the amber NDVI and of the Inverse Simple Ratio (ISR; Peng Gong et al., 2003) and reported that canopy sensors can be effectively used to improve N management based on the use of variable application rates in maize regardless of the vegetation index that is used. ...
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Methods for estimating the nitrogen (N) response of sugarcane while considering a variable rate of N application are required to allow for improved N use efficiency and higher yields. The objectives of this work were to compare the performance of three vegetation indices obtained from canopy sensor data to assess N in sugarcane fields at various crop stalk height as well as to determine the ability of these in-season sensor readings to predict the response of yield to N. Seven experiments were conducted in Brazil under different site conditions from 2009 to 2011. The treatments comprised five N application rates ranging from 0 to 200 kg N ha-1. A CropCircle canopy sensor (model ACS-210, Holland Scientific Inc., Lincoln, NE, USA) was used to determine the amber normalized difference vegetation index (aNDVI), chlorophyll index (CI), and inverse of the simple ratio (ISR) at different crop stalk heights. The N application increased the yield in only three of seven fields (p<0.1). The best results in terms of the canopy sensor readings, chlorophyll and leaf N content were found when the average crop stalk height was between 0.4 and 0.7 m. A weak relationship was observed between the vegetation indices and N from tissue analyses. The best results were obtained with the ISR due to its high r2, low RMSE, and consequently higher SEq with respect to sugarcane yield. The canopy reflectance sensor is a useful tool for identifying the variability within fields as well as to determine the variable rate of in-season N fertilizer application in sugarcane fields.
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ABSTRACT,and 10% in corn (Hilton et al., 1994). Fertilizer N losses due to surface runoff range between 1 and 13% In 2001, N fertilizer prices nearly doubled as a result of increased (Blevins et al., 1996; Chichester and Richardson, 1992). natural gas prices. This was further troubling when considering that the world N use efficiency (NUE) in cereal grain production averages,Urea fertilizers applied to the surface without incorpo- only 33%. Methods to improve NUE in winter wheat Response Index $15.9 billion annual loss of N fertilizer (Raun and John- son, 1999). With the increasing costs of N fertilizer due Evaluation of grain yield response to N fertilization to natural gas shortages, the unaccounted 67% is now in 15-yr corn and 30-yr wheat experiments has shown estimated to be worth more than $20 billion annually.,that check plots where no N has been applied exhibit Considering these poor use efficiencies and the associ-,wide variation in the supply of soil N from year to year ated costs of improper management, technological ad- (Johnson and Raun, unpublished, 2002). This temporal vances,are needed,to reduce,excess nutrient applica-,dependence,of N availability reinforces the need,for tions.,midseason measurements that account for N supplied through,mineralization. Raun et al. (2001) developed
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