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Spectral indices with different spatial resolutions in recognizing soybean phenology
Spectral indexes in recognizing soybean phenology
https://orcid.org/0000-0001-6918-5232
da Silva Airton Andrade Conceptualization Data curation Formal analysis Investigation Methodology Resources Software Validation Visualization Writing – original draft Writing – review & editing 1
https://orcid.org/0000-0001-9917-6863
Silva Francisco Charles dos Santos Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Resources Software Supervision Validation Visualization Writing – original draft Writing – review & editing 1 *
https://orcid.org/0000-0001-9101-0711
Guimarães Claudinei Martins Formal analysis Funding acquisition Investigation Methodology Project administration Supervision Validation Visualization Writing – original draft Writing – review & editing 2
Saleh Ibrahim A. Conceptualization Formal analysis Funding acquisition Investigation Methodology Resources Validation Visualization Writing – original draft Writing – review & editing 3
da Crus Neto José Francisco Data curation Formal analysis Investigation Methodology Resources Software Validation Visualization Writing – original draft Writing – review & editing 1
El-Tayeb Mohamed A. Conceptualization Formal analysis Funding acquisition Investigation Methodology Resources Software Visualization Writing – original draft Writing – review & editing 4
Abdel-Maksoud Mostafa A. Formal analysis Funding acquisition Investigation Methodology Resources Software Validation Visualization Writing – original draft Writing – review & editing 4
https://orcid.org/0000-0002-7308-0967
González Aguilera Jorge Formal analysis Investigation Methodology Software Validation Visualization Writing – original draft Writing – review & editing 5
AbdElgawad Hamada Formal analysis Funding acquisition Investigation Methodology Resources Software Validation Visualization Writing – original draft Writing – review & editing 6
Zuffo Alan Mario Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Resources Software Supervision Validation Visualization Writing – original draft Writing – review & editing 1 *
1 Universidade Estadual do Maranhão, Balsas, Maranhão, Brazil
2 Instituto Federal Goiano, Morrinhos, Goiano, Brazil
3 Faculty of Science, Zarqa University, Zarqa, Jordan
4 Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia
5 Universidade Estadual de Mato Grosso do Sul, Cassilandia, Mato Grosso do Sul, Brazil
6 Integrated Molecular Plant Physiology Research, Department of Biology, University of Antwerp, Antwerp, Belgium
da Silva Claudionor Ribeiro Editor
Universidade Federal de Uberlandia, BRAZIL
Competing Interests: NO authors have competing interests.

* E-mail: franciscocharlessilva@professor.uema.br (FCSS); alan_zuffo@hotmail.com (AMZ)
18 9 2024
2024
19 9 e030561023 1 2024
3 6 2024
© 2024 da Silva et al
2024
da Silva et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

The aim of the present research was to evaluate the efficiency of different vegetation indices (VI) obtained from satellites with varying spatial resolutions in discriminating the phenological stages of soybean crops. The experiment was carried out in a soybean cultivation area irrigated by central pivot, in Balsas, MA, Brazil, where weekly assessments of phenology and leaf area index were carried out. Throughout the crop cycle, spectral data from the study area were collected from sensors, onboard the Sentinel-2 and Amazônia-1 satellites. The images obtained were processed to obtain the VI based on NIR (NDVI, NDWI and SAVI) and RGB (VARI, IV GREEN and GLI), for the different phenological stages of the crop. The efficiency in identifying phenological stages by VI was determined through discriminant analysis and the Algorithm Neural Network–ANN, where the best classifications presented an Apparent Error Rate (APER) equal to zero. The APER for the discriminant analysis varied between 53.4% and 70.4% while, for the ANN, it was between 47.4% and 73.9%, making it not possible to identify which of the two analysis techniques is more appropriate. The study results demonstrated that the difference in sensors spatial resolution is not a determining factor in the correct identification of soybean phenological stages. Although no VI, obtained from the Amazônia-1 and Sentinel-2 sensor systems, was 100% effective in identifying all phenological stages, specific indices can be used to identify some key phenological stages of soybean crops, such as: flowering (R1 and R2); pod development (R4); grain development (R5.1); and plant physiological maturity (R8). Therefore, VI obtained from orbital sensors are effective in identifying soybean phenological stages quickly and cheaply.

http://dx.doi.org/10.13039/501100021557 College of Food and Agriculture Sciences, King Saud University RSPD2024R678 El-Tayeb Mohamed A. CAPES, Coordination for the Improvement of Higher Education Personnel 88887.677482/2022-00 https://orcid.org/0000-0001-6918-5232
da Silva Airton Andrade This study was funded by the College of Food and Agriculture Sciences, King Saud University, RSPD2024R678 (to Mohamed A. El-Tayeb). This study was also funded by a scholarship from CAPES, Coordination for the Improvement of Higher Education Personnel, 88887.677482/2022-00 (to Airton Andrade da Silva). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data AvailabilityAll relevant data is available in the GitHub repository at the following link: https://github.com/FSilva-826/DADOS---AMAZONIA1-CENTINEL2.
Data Availability

All relevant data is available in the GitHub repository at the following link: https://github.com/FSilva-826/DADOS---AMAZONIA1-CENTINEL2.
==== Body
pmcIntroduction

Soybean [Glicyne max (L.) Merril] is a crop of high economic importance, due to the revenue generated from its numerous uses and by-products created; social importance, due to the generation of employment resulting in regional development, and, mainly, for guaranteeing global food security, as it can be used in human and animal nutrition [1]. Therefore, research aimed at adding technologies and knowledge within the soy-bean production system, seeking to increase yield, is essential.

In this aspect, precision agriculture has become an ally in the process of optimizing soybean production, assisting in decision-making regarding agricultural practices based on the interpretation and integration of information from different data sources [2]. However, the aforementioned technique has presented deficiencies, in part, due to lesser efforts in incorporating the physiological principles of crop responses to environmental variation, such as crop phenology [3].

Soybean phenology is an important variable to be considered in decision-making during crop conjunction, such as choosing the sowing date, irrigation management decisions [4], lodging management [5], foliar application of nutrients [6], and/or phytosanitary management [7]. However, identifying the correct phenological stage in large areas and with different sowing dates becomes an expensive task, which demands time and technical knowledge.

Faced with this problem, vegetation indexes (VI) can be tools used to monitor soybean phenology during plant development, based on estimating the amount of electromagnetic energy reflected by the crop canopy. The technique can be used after interaction with pigments, water and intercellular spaces inside the leaf, providing data on the physiological state of the plant [8].

Among the potential advantages of using VI for monitoring soybean phenology is the speed in obtaining images, which are made available free of charge in catalogs and on mission websites, and the low demand for labor for the evaluation [9].

In recent years, different studies have demonstrated the efficiency of using of VI in differentiating the phenological stages of different crops, such as the normalized differentiation vegetation index (NDVI) used in soybean cultivation [10, 11]; the Normalized Differentiation Water Index (NDWI), for corn and soybeans [9]; the soil-adjusted vegetation index (SAVI), in wheat, corn and soybeans [12, 13]; the visible atmospheric resistance index (VARI), for corn and soybeans [14]; the visible green atmospheric resistance index (IV GREEN), in rice [15]; and the green leaf index (GLI), in corn [16].

The VARI, IV GREEN and GLI indices, as they contain only the red, green and blue bands in their composition, are classified as RGB indices. These consider wavelengths of the visible spectrum that relate only to leaf pigments (chlorophyll, carotenoids, anthocyanins and xanthophylls), which are responsible for absorbing 80–90% of visible light, with a peak of 0.55 μm, and reflect 10–20% of visible light, mainly the green band [17].

The NDVI, NDWI and SAVI indices have information from the near-infrared (NIR) range in their composition, being classified as NIR indices. This spectral range is very sensitive to the variation in vegetation biomass and, consequently, to the variation in plant growth and development [17].

Although there are many studies on the efficiency of different VIs in identifying the different phenological stages of soybeans and the evaluation of different sensor systems, with different spatial resolutions, are scarce, as the available studies use only one source of spectral data. Spatial resolution corresponds to the area on the ground represented in a single pixel of a digital image.

Given the above, the aim of the present research was to evaluate the efficiency of different vegetation indices (VI) obtained from satellites with varying spatial resolutions in discriminating the phenological stages of soybean crops.

Material and methods

Experiment location

The present research was carried out between April and July 2022, in a commercial production area of soybean seeds, with 80 ha, located 30 km from the municipality of Balsas, MA, Brazil (Fig 1), where the soybean, cultivar TMG2383, was conducted under central pivot irrigation. The location is at 7° 31’ 59’’ South, 46° 2’ 6’’ West, 243 meters altitude and has a rainy tropical climate (Aw) and an average temperature of 27.1 °C, according to Köppen climate classification. The average annual rainfall is 1175 mm, with the highest rainfall in the months of November to April, when they account for 85% of the total [18].

10.1371/journal.pone.0305610.g001 Fig 1 Location of the experiment area.

Maps of the Maranhão State (A), Brazil (B) and the experimental area location (C). Source: [19].

The soil in the experimental area was classified as Red Oxisol [20]. Meteorological data on average temperature, total precipitation and cloudiness for the study period (Fig 2) were obtained by the meteorological station of the Brazilian National Institute of Meteorology (INMET), present in the study region [21].

10.1371/journal.pone.0305610.g002 Fig 2 Weather conditions during the experiment.

Total precipitation, cloudiness and average temperature, during the months which the experiment was carried out, in Balsas, MA, in the year 2022. Source: [21].

Conducting the experiment

The experiment was carried out in a randomized block design, with four replications. Each measure point (replication) consisted of an area of 5 meters in radius, for Sentinel-2 products, and 30 meters in radius, for Amazonia-1 products, whose centers were demarcated with a global positioning system (GPS) navigation receiver.

After soybean germination, weekly monitoring of plant phenology was carried, according to [22] (Table 1) and also the leaf area index, using the dry disc method [23], adapted to a cylinder with a section of 7 cm2 and 10 discs per sample of three plants.

10.1371/journal.pone.0305610.t001 Table 1 Soybean phenological stages.

STAGE	DESCRIPTION	
V1	A pair of unifoliate leaves (or a node);	
V2	First fully developed trefoil (or two nodes);	
V3	Two fully developed trefoils (or three nodes);	
Vn	Vegetative stages until reaching the reproductive stage, from the emission of the first floral bud;	
R1	An open flower at any node on the main stem;	
R2	An open flower on one of the two upper nodes of the main stem, with a fully developed leaf;	
R3	Pod 0.5 cm to 2.0 cm in one of the four upper nodes of the main stem;	
R4	Pod fully developed (> 2.0 cm) on one of the four upper nodes of the main stem;	
R5.1, R5.2, R5.3, R5.4 e R5.5	Beginning of grain filling (<10% to 100% grain) in one of the four upper nodes of the main stem;	
R6	Full or complete grain in one of the four upper nodes of the main stem;	
R7	Beginning of maturation: a pod with a mature color on the main stem;	
R8	Full maturity (harvest): more than 95% of the pods are ripe in color.	
Source: [22]

Images collected by the Multispectral Imager (MSI) sensor, onboard the Sentinel-2 satellite, were used as a source of reflectance data, which were obtained from the image catalog of the Copernicus Open Access Center (https://scihub.copernicus.eu/dhus/#/home), at L2A processing level. The L2A images were corrected for clouds and cloud shadows, aerosol optical thickness, water vapor, orthorectified surface reflectance with multispectral, and subpixel multitemporal registration accuracy. The sensor provided products with a temporal resolution of 5 days, spatial resolution, which varies from 10 m to 65 m, radiometric resolution of 10 bits and spectral resolution of 13 bands, however, in the present study, only bands with a spatial resolution of 10 were used. meters (Table 2).

10.1371/journal.pone.0305610.t002 Table 2 Technical specifications of the MSI sensor, onboard the Sentinel-2 satellite, and the WFI sensor, onboard the Amazonia-2 satellite.

Satellite	Temporal
Resolution	Spectral
Resolution (μm)	Radiometric
Resolution	Spatial Resolution	
Sentinel-2	5 days*	Blue (0.49–0.56)	10 bits	10 m	
Green (0.56–0.67)	
Red (0.67–0.84)	
Near Infrared (0.84–0.87)	
Amazonia-1	5 days	Blue (0.45–0.52)	10 bits	~ 65 m	
Green (0.52–0.59)	
Red (0.63–0.69)	
Near Infrared (0.77–0.89)	
* Resolution of constellation with Multispectral Imager (MSI) sensor

Images from the WFI (Wide Field Imager) orbital sensor, onboard the Amazonia-1 satellite, were also used as a source of reflectance data, which were obtained from the image catalog of INPE—National Institute for Space Research (http://www2.dgi.inpe.br/catalogo/explore). The sensor provided products with resolution in accordance with Table 2.

Both satellites were chosen due to their temporal resolution of 5 days, which allows better monitoring of phenology throughout the crop cycle, and spatial resolution of 10 and 64 m, enabling comparison of data quality. Because, according to [24], monitoring phenology, using orbital sensors, must have the greatest amount of data during the cycle, favoring the monitoring of the different stages of the crop.

The temporal resolution of the sensors allowed the collection of 14 images for the Sentinel-2 satellite and 11 images for the Amazonia-1 satellite, throughout the crop cycle, which made it possible to obtain spectral information for the phenological stages from V3 to R8 (Fig 3). The criteria for selecting images followed the availability for download of the respective image catalogs and that they did not present total cloud coverage in the study area.

10.1371/journal.pone.0305610.g003 Fig 3 Phenological stages of soybean crop.

Morphological aspect of soybeans at different phenological stages, from which spectral information was obtained by the Sentinel-2 and Amazonia-1 satellites. Source: Adapted from [22].

Image processing

The images obtained were processed in Qgis software version 3.26.3 [25] using Datum Sirgas 2000 UTM Zone 23S as the coordinate system. The images were submitted to the VI equations (Table 3), generating the products presented in Figs 4 and 5.

10.1371/journal.pone.0305610.g004 Fig 4 Behavior of vegetation indices (VIs) throughout the soybean cycle, for Senti-nel-2 products, and for each phenological stage (PS) of the crop.

10.1371/journal.pone.0305610.g005 Fig 5 Behavior of vegetation indices (IVs) throughout the soybean cycle, for Ama-zonia-1 products, and for each phenological stage (PS) of the crop.

10.1371/journal.pone.0305610.t003 Table 3 Vegetation indices (VIs) used in the present research.

Type	Vegetation
Indices (Vis)	Name	Equation	Reference	
NIR	NDVI	Normalized Differentiation Vegetation Index	(B8-B4)/(B8+B4)	[26]	
NDWI	Normalized Differentiation Water Index	(B3-B8)/(B3+B8)	[27]	
SAVI	Soil-Adjusted Vegetation Index	(1+L)*[(B8•B4)÷(B8+B4+L)]	[28]	
RGB	VARI	Visible Atmospheric Resistance Index	(B3-B4)/(B3+B4-B2)	[29]	
GLI	Green Leaf Index	(2*B3-B4-B2) / (2*B3+B4+B2)	[30]	
IV GREEN	Atmospheric resistance indices Visible Green	(B3-B4)/(B3+B4)	[29]	
Notes: B4: red reflectance, B8: near infrared reflectance, B3: green reflectance; B2: blue reflectance, L = adjustment factor that can vary from 0 to 1.

Statistical analyzes

With the help of the GENES statistical program [31], the data obtained were subjected to the F test of the analysis of variance (ANOVA), which sought to verify the existence of a significant difference at 1% and 5% probability between the repetitions and between the phenological stages, throughout the cycle. of the crop, for the different vegetation indices (VIs).

In order to quantify the efficiency of each VI in differentiating the phenological stages of soybean, the values of the IVs that showed differences between the phenological stages were subjected to discriminant analysis by [32] and an Artificial Neural Network (ANN) algorithm type Multilayer Perceptron [33], with the aid of Genes and Weka 3.8.5 software (Waikato, New Zealand), respectively. The objective was to allow the algorithms to distinguish the clusters that corresponded to each of the phenological stages, based on the field spectral data associated with each of them.

Discriminant analysis was initially addressed by [34], and consists of obtaining mathematical functions capable of classifying a sample into one of several groups based on characteristics of these groups, seeking to minimize the probability of misclassification, that is, minimizing the probability of classifying mistakenly an individual in one population, when it actually belongs to another population. This analysis is a multivariate statistical technique used to discriminate and classify objects (Eq 1).

Dix˜=Li´˜.x˜−12.Li´˜.μi˜+lnpi (1)

Where: Dix˜= population discriminant function ‘i’ of the random vector x˜; Li´˜ = population discriminant random vector ‘i’; x˜= random feature vector; μi˜= vector of population means ‘i’; pi = population probability of occurrence ‘i’.

Before applying the discriminant analysis, the data were subjected to the analysis of normality, using the Kolmogorov-Smirnov test (having presented normality), of identification of outliers through box plot graphs (these being removed when identified), as well as the Box’s M test, to check the similarity of the dispersion matrices of the independent variables.

In turn, ANN was adopted because the pattern of variation in VIs throughout the soybean development cycle required non-linear statistical modeling. The ANN was tested using the standard architecture of the Weka 3.8.5 software, consisting of a Multilayer Perceptron, with a single hidden layer, formed by a number of neurons equal to the sum of the number of variables analyzed with the number of classes, divided by two, and using cross-validation with k-fold = 10.

The efficiency of the discriminant functions in correctly classifying each phenological stage was estimated by the apparent error rate (Eq 2), where higher values indicate lower accuracy in the differentiation. APER=1N.∑i=lnmi (2)

Where: APER = apparent error rate; mi = number of wrong observations in groups; N = total number of ratings.

In the present study, only the VI that presented an apparent error rate equal to zero were considered efficient, that is, they allowed the correct identification of a stadium in 100% of the samples.

Methodology summary

A summary of the methodology used in this research is illustrated in Fig 6.

10.1371/journal.pone.0305610.g006 Fig 6 Flowchart of the present research methodology.

Results

All vegetations indices (VIs) behaved in accordance with the standard re-ported in the literature [35–37], demonstrating the quality of the spectral information obtained (Fig 7).

10.1371/journal.pone.0305610.g007 Fig 7 Variation of NIR and RGB vegetation indices from the Sentinel-2 and Amazonia-1 satellites.

Vegetation Indices NDVI, NDWI, SAVI (NIR type), and VARI, GLI and IV GREEN (RGB type). Notes: ns = not significant; ** = significant at 1% by the F test from ANOVA.

All VI analyzed, in both sensor systems (Sentinel-2 and Amazonia 1), showed significant differences between the phenological stages, at 5% probability, by the F test of the analysis of variance (Fig 7), making it possible to quantify the efficiency of distinguishing the phenological stages of the soybean crop for each VI, via discriminant analysis.

For the Sentinel-2 sensor system, the discriminant analysis for the NDVI, NDWI and SAVI indices showed the highest apparent error rates (APER), with 62.5, 60.7 and 62.5%, respectively, or that is, less than 50% efficiency in identifying the correct phenological stages. The IV GREEN index provided the best efficiency in correctly identifying phenological stages, with an APER of only (56.2%). On the other hand, using the ANN algorithm, the NDWI index was the most efficient (47.4%) for APER (Table 4).

10.1371/journal.pone.0305610.t004 Table 4 Apparent Error Rate (APER, %) of vegetation indices (VIs) for the Sentinel-2 and Amazonia-1 sensor systems, obtained via Anderson Discriminant Functions and Multilayer Perceptron Neural Network.

Satellite	Anderson Discriminant Functions	Multilayer Perceptron Neural Network	
NIR	RGB	NIR	RGB	
Sentinel-2	NDVI = 62.5	VARI = 59.8	NDVI = 51.8	VARI = 63.4	
NDWI = 60.7	GLI = 58.9	NDWI = 47.4	GLI = 67.9	
SAVI = 62.5	IV GREEN = 56.2	SAVI = 65.2	IV GREEN = 57.1	
Amazonia-1	NDVI = 70.4	VARI = 63.6	NDVI = 65.9	VARI = 73.9	
NDWI = 53.4	GLI = 60.2	NDWI = 60.2	GLI = 58.0	
SAVI = 57.9	IV GREEN = 64.7	SAVI = 53.4	IV GREEN = 72.8	

For the Amazonia-1 sensor system products, the discriminant analysis for NDVI, IV GREEN, VARI, GLI and SAVI showed the highest APER, 70.4, 64.7, 63.6, 60.2 and 57.9%, respectively, being the lowest for the NDWI index (53.4%) (Table 4).

The APER of the discriminant analysis varied between 53.4% and 70.4% while the APER of the ANN was between 47.4% and 73.9%. Therefore, it was not possible to identify which of the two analysis techniques (discriminant analysis or ANN) is more appropriate for processing spectral data aiming to identify soybean phenological stages, since the aforementioned techniques present variation in APER depending on the satellite or VI to be considered, and the combination between them (Table 4).

No VI obtained from the Amazonia-1 and Senti-nel-2 sensor systems was 100% effective in identifying the phenological stages of soybean crops, which demonstrates the impossibility of using these indices to simultaneously identify all stages of culture. On the other hand, some indices showed efficiency of 100% in identifying specific phenological stages (Figs 8 and 9).

10.1371/journal.pone.0305610.g008 Fig 8 Correct classifications of soybean phenological stages using different vegetation indices via discriminant analysis.

Vegetation Indices NDVI, NDWI, SAVI (NIR type), and VARI, GLI and IV GREEN (RGB type) from the Sentinel-2 and Amazonia-1 satellites.

10.1371/journal.pone.0305610.g009 Fig 9 Correct classifications of soybean phenological stages using different vegetation indices via multilayer perceptron neural network.

Vegetation Indices NDVI, NDWI, SAVI (NIR type), and VARI, GLI and IV GREEN (RGB type) from the Sentinel-2 and Amazonia-1 satellites.

No VI proved to be statistically efficient in identifying the vegetative stages of soybean development, with the most efficient ones showing a maximum effectiveness of 87.5% (Figs 8 and 9).

The correct classification of the stages that mark the flowering of the soybean crop, R1 (beginning of formation) and R2 (full flowering) was effective based on data from the Sentinel-2 and Amazonia-1 Sensor System (Fig 8). The R1 stage was successfully identified by the Amazonia-1 SAVI index, when submitted to the ANN Algorithm (Fig 9). The R2 stage was correctly identified by the NIR-based indices (NDVI and SAVI) of Sentinel-2 and the RGB-type indices (IV Green and VARI) of the Amazonia-1 sensor system, when Discriminant Analysis was applied (Fig 8). The NDWI index, obtained from the Sentinel-2 satellite, was also effective in identifying the R2 stage, when analyzed by the ANN Algorithm (Fig 9).

As for the reproductive stages, which mark the development of the pods, only the R4 stage (fully formed pod) was successfully identified by the Amazonia-1 sensor, using the IV GREEN index, when analyzed by Discriminant Analysis (Fig 8).

The R5.1 stage, which characterizes the development of grains inside the pods, was precisely identified using spectral information from the Sentinel-2 satellite, using the NDWI index, analyzed by Discriminant Analysis (Fig 8). The aforementioned stage (R5.1) was also identified by the NDWI and SAVI indices, obtained based on data from Amazonia-1, when analyzed via the ANN Algorithm or Discriminant Analysis (Figs 8 and 9).

In turn, the R8 stage (full maturity of the soybean crop) was correctly identified only by the GLI index, obtained from spectral data from the Amazonia-1 satellite, regardless of the analysis technique, ANN Algorithm or Discriminant Analysis (Figs 8 and 9).

Regarding the performance of the statistical techniques used, despite the ability of Multilayer Perceptron Neural Network algorithms (ANN) to identify non-linear patterns in a set of data, it was not possible to verify the superiority of this technique over Discriminant Analysis, once both of which have similar efficacy.

Discussion

The quality of the spectral information obtained (Fig 7) was confirmed because the behavior of all vegetations indices (VIs) is in accordance with the standard reported in the literature [36–39].

Several authors report that the spectral behavior of soybeans throughout the cycle presents low values at the beginning of the cycle, gradually increases to a maximum biomass and decreases with the end of the crop cycle [10, 12–16, 39]. The NDWI index showed behavior similar to that mentioned above, however the curve was inverse due to its values being negative, as reported by [9]. The significant differences between the phenological stages for all VI analyzed (Fig 7), in both sensor systems (Sentinel-2 and Amazonia 1), made it possible to quantify the efficiency of distinguishing the phenological stages of the soybean crop for each VI, via discriminant analysis. [40], were able to classify different characteristics to differentiate soybean cultivars, based on the discriminant function, with a lower apparent error rate.

Although no VI obtained from the Amazonia-1 and Sentinel-2 sensor systems was 100% effective in simultaneously identifying all phenological stages of the soybean crop. Some indices made it possible to correctly identify stages R1, R2, R4, R5.1and R8.

In turn, the precise identification of stages R1 and R2 is essential for determining the appropriate time to apply nutritional and phytosanitary management measures and productivity prediction. For example, the R1 stage of soybean cultivars with determinate growth, and R2, for cultivars with indeterminate growth, are ideal for evaluating the nutritional status of the plant, by foliar diagnosis [41]. At these stages (R1 and R2), pesticide applications are also recommended to control white mold (Sclerotinia sclerotiorum), Asian rust (Phakopsora pachyrhizi), Brown spot (Septoria glycines), Powdery mildew (Erysiphe diffusa), Black spot target (Corynespora cassiicola) and Anthracnose (Colletotrichum truncatum) [42]. [43] defined the R1 and R2 stages as the best moment for predicting productivity in soybean crops, based on the Normalized Difference Vegetation Index (NDVI).

As for the R4 stage, [44] defined it as the best time for predicting productivity in soybean crops based on the Soil Adjusted Vegetation Index (SAVI). At this stage, which marks the most critical beginning of development in terms of yield, supplemental irrigation may be a recommended practice with the aim of reducing abortion [45].

The correct identification of phenological stage R5.1 is important because they are characterized by maximum nutrient and water requirements. Also, supplementary irrigation management is recommended in case of water deficit, favoring the absorption of nutrients from the soil, in addition to enriching seeds with molybdenum and predicting productivity, based on the Visible Atmospheric Resistance Index [45–47].

The R8 stage must be correctly identified as it represents an ideal time for applying desiccants, favoring water loss of up to 13–14% of grain moisture, which provides an adequate soybean harvest [46].

The VIs discussed in this study fall into two categories: RGB in-dexes and indices that are based on the near-infrared (NIR). The VARI, IV GREEN and GLI indices, which are composed only of the blue, green and red bands, are categorized as RGB indices, and are associated only with leaf pigments (chlorophyll, carotenoids, anthocyanins and xanthophylls). On the other hand, the NDVI, NDWI and SAVI in-dexes include information from the near-infrared (NIR) range, which is highly sensitive to variation in plant biomass and, consequently, to variation in plant growth and development [17]. The relationship of these indices with morphophysiological aspects of plants explains their ability to identify changes in the previously mentioned soybean phenological stages.

The sensor systems studied showed similar effectiveness in identifying soybean phenological stages, both being capable of identifying stages R2, R4 and R5.1. However, the Amazonia-1 satellite showed superiority with the precise identification of the V3 and R8 stages, demonstrating that the spatial resolution of 64 meters, of the Wide Field Imager (WFI) sensor embargoed on this satellite, is not a limiting factor for the identification of some phenological stages of soybeans, at least in large cultivation areas, as was the case in this study. However, studies are needed to investigate the efficiency of this sensor system in identifying soybean phenological stages with variation in the imagined cultivation area.

[48] managed to differentiate the initial stages of growth of soybeans and corn, using the spatial resolutions of 10 and 30 m of the MSI sensor on the Sentinel-2 satellite, demonstrating that different spatial resolutions have similar effectiveness for monitoring phenology in soybeans.

Regarding the types of VIs for the reproductive stages R1, R2, R4 and R5.1, the RGB indices were as efficient as the indices that have the NIR band.

RGB indices such as VARI, VIGREEN and GLI have received a lot of attention due to the possibility of their application based on data collected by simple and low-cost cameras that can be mounted on unmanned aerial vehicles (UAVs) [49]. Thus, the results of this research open the possibility of using RGB sensors in UAVs to differentiate the reproductive stages of soybeans with greater practicality and agility, as has already been observed by [50].

[51, 52] e [53] observed that RGB-type indices, compared to indices that use the infrared band, demonstrated greater yield for the concise prediction of grain yield, concentration of nitrogen and phosphorus in the leaf, proportion of carbon to nitrogen, under a wide range of nitrogen fertilization levels, and effects of phosphate fertilizer applications, in corn cultivation, in addition to potential use for selection of soybean cultivars with drought tolerance.

Another important point observed in the present study is that, regardless of the type of sensor system or type of vegetation index, the highest rates of effectiveness in classifying phenological stages were obtained for the reproductive stages of R1, R2 (flowering), R4 (developed pod), R5.1 (grain development). The possible explanation for this result lies in the variation in the magnitude of VI values throughout the crop cycle.

The spectral behavior of soybeans throughout the cycle presents low values at the beginning of the cycle, gradually increases and decreases as the end of the crop cycle approaches (Fig 7), corroborating [10, 12–16, 39]. The same RGB indices, which present a smaller magnitude of variation in their values throughout the crop cycle, also presented the same pattern of variation as observed for the VARI index of the Sentinel-2 satellite (Fig 7).

This aforementioned spectral behavior results in the proximity and even coincidence of the values of the VIs between the initial and final stages, as well as the similarity between the values of the indices in the final reproductive stages, R6 to R8. This similarity of values makes it impossible to effectively distinguish between the initial phenological stages and the final stages of crop development. This leaves the reproductive stages from R1 to R5.1, with more divergent values for the indices than the other stages, which results in higher efficiency rates in identifying these phenological stages. Similar results were obtained for rice by [54] using machine learning to identify the phenological stages of the crop.

To corroborate these results, the same pattern of variation is observed for the leaf area index—LAI (Fig 10). The LAI is defined as the relationship between the leaf area of a plant and the soil area occupied by it, with a high correlation between spectral in-dexes [22, 55].

10.1371/journal.pone.0305610.g010 Fig 10 Box plot for the VARI vegetation index, from the Sentinel-2 satellite, and for the soybean.

(LAI) leaf area index.

Several authors, when studying the spectral behavior of soybeans, corn, wheat and rice, using RGB and Infrared vegetation indices, observed little variation in the values of the indices during the reproductive stages [9–16].

Although no VI was efficient in identifying all phenological stages of soybean crops, the indices were effective in identifying specific stages, demonstrating the possibility of using the technique in managing soybean crops. Furthermore, the free availability of reflectance data as well as processing software makes the non-use of this method of monitoring the development of soybean crops unjustified.

However, new studies that encompass other VI, as well as the application of combinations and/or simultaneous use of vegetation indexes to refine the identification of soybean phenological stages via remote sensing are necessary.

Furthermore, due to the pattern of variation in VI throughout the soybean development cycle, studies involving non-linear statistical modeling must be carried out in order to refine the identification of phenological stages via VI.

Conclusions

Vegetation indices, obtained from orbital sensors, are effective in identifying soybean phenological stages relatively quickly and cheaply.

The RGB and NIR vegetation indices can be used to precisely identify some key phenological stages for soybean crop management, such as flowering (R1 and R2), end of pod development (R4), the stage that characterizes the development of grains inside the pods (R5.1) and stage that marks the physiological maturity of the crop (R8).

No vegetation index, obtained by the Amazônia-1 and Sentinel-2 sensor systems, was 100% effective in identifying all phenological stages of the soybean crop.

The study suggests that the difference in spatial resolution of the two sensors evaluated, 10 meters per pixel of Sentinel-2 and 65 meters per pixel of Amazônia-1, is not a determining factor in the correct identification of soybean phenological stages.

10.1371/journal.pone.0305610.r001
Decision Letter 0
Silva Claudionor Ribeiro da Academic Editor
© 2024 Claudionor Ribeiro da Silva
2024
Claudionor Ribeiro da Silva
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
28 Feb 2024

PONE-D-24-03115Spectral indexes with different spatial resolutions in recogniz-ing soybean phenologyPLOS ONE

Dear Dr. Silva,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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1. The goal of the study is not clear. What the key research questions that your work address?

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3. It was very difficult to see whether the work effectively addressed the aim of the study in the conclusion.

4. To include some statistical findings in your abstract.

5. Performing exploratory data analysis to understand the variability within vegetation indexes across different phenological stages could be beneficial.

6. Verify the assumptions underlying Anderson's discriminant analysis, such as multivariate normality and homogeneity of covariance matrices, through diagnostic tests or graphical methods.

7. Evaluate the efficiency of vegetation indexes, compare the performance of the discriminant analysis model with baseline classifiers such as logistic regression or support vector machines.

8. Conduct sensitivity analysis to examine the impact of different parameters or settings on the results of Anderson's discriminant analysis.

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Reviewer #1: Partly

Reviewer #2: Partly

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Major concern

1. The goal of the study is not clear to mean, or have not been well articulated in the last paragraph of the introduction. In fact, what the key research questions that your work seek to address?

2. The methodology is too shallow and has no debt to allow readers identify the novelty or contribution of the work. So much effort was spent on describing the data used, even in the aspect that talked about “Conducting the experiment.” Readers are expecting to see a step-by-step description of how the experiment was conducted. It was difficult to connect flowchart in Figure 1 with the method description. Every aspect of the work is just mixed up with content that ought to be in different section. In this current form, the work lack is no logical coherence. For example, I see you have two figures with caption as Figure 1. Secondly, for the second Figure 1 (flowchart), how was the reprojection done for both satellite images? How was the field data corroborated with the satellite image using the Anderson Discriminant Analysis? Lastly, what do you mean by Conclusions? You flowchart is supposed to be a concise summary of the approach adopted. But here, you confuse readers with “Conclusions”. How are we supposed to understand that while reading your methodology? Please, streamline the methodology to allow for easy reproducibility.

3. While reading your conclusion, it was very difficult to tell if the work actually addressed the goal of the study. I suggest you overhaul the entire manuscript, and then, clearly state the goal or objectives of the study. Then, try to show us in the conclusion that the goal has been achieved.

Minor comment

1. Why did you hyphenate “recogniz-ing” in the title (line 2)?

2. The title of this manuscript can be improved. Think about it carefully.

3. What do you mean by “Search Location” in line 100? Note that "Search location" and "Study location" are not exactly the same.

4. It would be very nice to include some statistical findings in your abstract.

Reviewer #2: Title: Spectral indexes with different spatial resolutions in recogniz-ing soybean phenology.

The title is not correct. "Indices" is the plural form of "index" when used in the context of measurements or indicators. Secondly the use of the hyphen in ‘recogniz-ing’ is unnecessary in this context. Similar mistake is also repeated in the Abstract section too, like: ‘cul-tivation’, and ‘demon-strates’. Therefore, the accurate title for the paper would be: "Spectral Indices with Different Spatial Resolutions in Recognizing Soybean Phenology." This usage adheres to standard scientific terminology in the field of remote sensing and geographic information systems.

In the abstract section, the findings are verbally expressed without substantiating the same with data. This is not a standard practice. The authors should have briefly described with data (in the abstract section) how different indices are correlated in identifying phonological stages of the soybean crop.

The spatial resolution of Amazonia 1 and sentinel 2 for (NIR, RGB) are 60 m and 10 m respectively. For a better accuracy assessment, the efficiency of each spectral indices needs to be checked at the same spatial resolution for both Amazonia 1 and sentinel 2 imageries, using resampling technique. This can be done either by upscaling the 10 m to 60 m or downscaling the 60 m to 10 m resolution. The authors have ignored this fact.

The temporal resolution i.e. the revisit frequency of each single SENTINEL-2 satellite is 10 days while the combined constellation revisit time is 5 days. The authors seem to have confused the revisit time of Sentinel 2 constellation with the temporal variation of MSI sensor onboard Sentinel-2. This is a major correction and it will impact the findings of this study.

Reviewer #3: Dear Authors,

Thank you for choosing PLOS for your interesting study. However, here are my specific suggestions and comments:

1. Consideration of Additional Statistical Tests: While Anderson's discriminant analysis is valuable for assessing classification accuracy, incorporating additional statistical tests such as ANOVA or pairwise comparisons could provide further insights into the significance of differences between vegetation indexes and phenological stages. This would strengthen the statistical robustness of your findings.

2. Exploratory Data Analysis for Variability: Prior to conducting discriminant analysis, performing exploratory data analysis to understand the variability within vegetation indexes across different phenological stages could be beneficial. Box plots or histograms could help visualize the distribution of index values and identify any outliers or trends that may impact the analysis.

3. Assessment of Model Assumptions: Verify the assumptions underlying Anderson's discriminant analysis, such as multivariate normality and homogeneity of covariance matrices, through diagnostic tests or graphical methods. Addressing violations of these assumptions ensures the reliability of the classification results.

4. Validation Techniques for Model Performance: Consider employing cross-validation or bootstrap resampling techniques to validate the performance of the discriminant analysis model. This would assess the generalizability of the classification results and provide confidence in the effectiveness of the selected vegetation indexes for phenological stage identification.

5. Comparison with Baseline Models: In addition to evaluating the efficiency of vegetation indexes, compare the performance of the discriminant analysis model with baseline classifiers such as logistic regression or support vector machines. This comparative analysis would offer a broader perspective on the suitability of different statistical approaches for phenological stage classification.

6. Sensitivity Analysis for Model Parameters: Conduct sensitivity analysis to examine the impact of different parameters or settings on the results of Anderson's discriminant analysis. This analysis would help identify optimal parameter choices and enhance the reproducibility of the classification outcomes.

By incorporating these suggestions, you can enhance the rigor and validity of your statistical analysis, providing a more comprehensive assessment of the efficiency of vegetation indexes in distinguishing soybean phenological stages.

Thank you in advance for taking into consideration my commnts and suggestions.

Kind regards,

Reviewer

**********

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Reviewer #1: No

Reviewer #2: Yes: Dr. A Salim Khan

Reviewer #3: No

**********

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10.1371/journal.pone.0305610.r002
Author response to Decision Letter 0
Submission Version1
1 May 2024

Response to Reviewers

Regarding Figure 1, the satellite image obtained from Google Earth was replaced by an image collected by the CBERS4A satellite, a platform belonging to the Brazilian State that, like the LANDSAT system, has free distribution without copyright restrictions.

Reviewer #1:

1. The goal of the study is not clear to mean, or have not been well articulated in the last paragraph of the introduction. In fact, what the key research questions that your work seek to address?

The aim of the research was to evaluate the efficiency of different vegetation indexes, obtained from satellites with different spatial resolutions, in discriminating the phenological stages of soybean crops. We made the necessary changes to the original material to meet the reviewer's recommendations.

2. The methodology is too shallow and has no debt to allow readers identify the novelty or contribution of the work. So much effort was spent on describing the data used, even in the aspect that talked about “Conducting the experiment.” Readers are expecting to see a step-by-step description of how the experiment was conducted. It was difficult to connect flowchart in Figure 1 with the method description. Every aspect of the work is just mixed up with content that ought to be in different section. In this current form, the work lack is no logical coherence. For example, I see you have two figures with caption as Figure 1. Secondly, for the second Figure 1 (flowchart), how was the reprojection done for both satellite images? How was the field data corroborated with the satellite image using the Anderson Discriminant Analysis? Lastly, what do you mean by Conclusions? You flowchart is supposed to be a concise summary of the approach adopted. But here, you confuse readers with “Conclusions”. How are we supposed to understand that while reading your methodology? Please, streamline the methodology to allow for easy reproducibility.

After the review, figure 1 now corresponds to the study location and the methodology flowchart becomes figure 4.

The reprojection of images was only necessary for Sentinel-2 images that are originally available in the WGS 84 coordinate reference system, requiring reprojection to Datum Sirgas 2000 UTM Zone 23S, which corresponds to the UTM projection zone of the study area.

Data collected in the field were correlated with satellite images, both representing the same sampling moment, and were used to verify the possibility of identifying a certain phenological stage, without the need to go to the field. To investigate this possibility, Anderson's Discriminant Analysis was applied to the satellite images and, after corrections, the Neural Networks technique was also applied.

The term “Conclusions” in the image referred to the study conclusions, but, in reviewing the image, the term was replaced by an icon that represents the interpretation of the obtained results.

All corrections, suggested by the reviewer for the methodology, were accepted.

3. While reading your conclusion, it was very difficult to tell if the work actually addressed the goal of the study. I suggest you overhaul the entire manuscript, and then, clearly state the goal or objectives of the study. Then, try to show us in the conclusion that the goal has been achieved.

The conclusions were rewritten to be well adjusted to the objective, as suggested by the reviewer.

Minor comment

1. Why did you hyphenate “recogniz-ing” in the title (line 2)?

Hyphen removed from the title

2. The title of this manuscript can be improved. Think about it carefully.

Suggestion accepted.

3. What do you mean by “Search Location” in line 100? Note that "Search location" and "Study location" are not exactly the same.

The terms have been standardized.

4. It would be very nice to include some statistical findings in your abstract.

Suggestion accepted.

Reviewer #2:

1. Title: Spectral indexes with different spatial resolutions in recogniz-ing soybean phenology. The title is not correct. "Indices" is the plural form of "index" when used in the context of measurements or indicators. Secondly the use of the hyphen in ‘recogniz-ing’ is unnecessary in this context. Similar mistake is also repeated in the Abstract section too, like: ‘cul-tivation’, and ‘demon-strates’. Therefore, the accurate title for the paper would be: "Spectral Indices with Different Spatial Resolutions in Recognizing Soybean Phenology." This usage adheres to standard scientific terminology in the field of remote sensing and geographic information systems.

The reviewer's suggestion was fully met.

2. In the abstract section, the findings are verbally expressed without substantiating the same with data. This is not a standard practice. The authors should have briefly described with data (in the abstract section) how different indices are correlated in identifying phonological stages of the soybean crop.

The reviewer's suggestion was fully met.

3. The spatial resolution of Amazonia 1 and sentinel 2 for (NIR, RGB) are 60 m and 10 m respectively. For a better accuracy assessment, the efficiency of each spectral indices needs to be checked at the same spatial resolution for both Amazonia 1 and sentinel 2 imageries, using resampling technique. This can be done either by upscaling the 10 m to 60 m or downscaling the 60 m to 10 m resolution. The authors have ignored this fact.

In this work, the authors chose not to apply the resampling technique in order to simulate the real conditions of data use. So, when a user uses satellite image data, products are generated from images with their original spatial resolution, without resampling. Thus, it is possible to infer the influence of spatial resolution on the accuracy of vegetation indexes in identifying soybean phenological stages.

4. The temporal resolution i.e. the revisit frequency of each single SENTINEL-2 satellite is 10 days while the combined constellation revisit time is 5 days. The authors seem to have confused the revisit time of Sentinel 2 constellation with the temporal variation of MSI sensor onboard Sentinel-2. This is a major correction and it will impact the findings of this study.

The reviewer's suggestion was fully met.

Reviewer #3:

1. Consideration of Additional Statistical Tests: While Anderson's discriminant analysis is valuable for assessing classification accuracy, incorporating additional statistical tests such as ANOVA or pairwise comparisons could provide further insights into the significance of differences between vegetation indexes and phenological stages. This would strengthen the statistical robustness of your findings.

ANOVA was performed to identify differences between vegetation indexes at different phenological stages. However, the authors decided to present only the significance of its test (F Test) in figure 5. It was decided not to perform a pairwise comparison test, and, to represent the index values difference in the different phenological stages, it was chosen the line graph shown in figure 5.

2. Exploratory Data Analysis for Variability: Prior to conducting discriminant analysis, performing exploratory data analysis to understand the variability within vegetation indexes across different phenological stages could be beneficial. Box plots or histograms could help visualize the distribution of index values and identify any outliers or trends that may impact the analysis.

Before the ANOVA, box plot graphical analysis was performed to identify outliers, which were eliminated when identified. However, it was decided not to present such graphics in order to make the reading of the work more objective and fluid.

3. Assessment of Model Assumptions: Verify the assumptions underlying Anderson's discriminant analysis, such as multivariate normality and homogeneity of covariance matrices, through diagnostic tests or graphical methods. Addressing violations of these assumptions ensures the reliability of the classification results.

The reviewer's suggestion was fully met.

4. Validation Techniques for Model Performance: Consider employing cross-validation or bootstrap resampling techniques to validate the performance of the discriminant analysis model. This would assess the generalizability of the classification results and provide confidence in the effectiveness of the selected vegetation indexes for phenological stage identification.

The cross-validation technique was applied to the Neural Network Analysis that was added to the study.

5. Comparison with Baseline Models: In addition to evaluating the efficiency of vegetation indexes, compare the performance of the discriminant analysis model with baseline classifiers such as logistic regression or support vector machines. This comparative analysis would offer a broader perspective on the suitability of different statistical approaches for phenological stage classification.

The reviewer's suggestion was fully met and the data was also subjected to Neural Network Analysis. This technique is capable of identifying non-linear patterns in the data set and thus maximizing the identification of groups of samples.

6. Sensitivity Analysis for Model Parameters: Conduct sensitivity analysis to examine the impact of different parameters or settings on the results of Anderson's discriminant analysis. This analysis would help identify optimal parameter choices and enhance the reproducibility of the classification outcomes. By incorporating these suggestions, you can enhance the rigor and validity of your statistical analysis, providing a more comprehensive assessment of the efficiency of vegetation indexes in distinguishing soybean phenological stages.

Specifically, for the suggestion of carrying out sensitivity analyzes to examine the impact of different parameters or configurations on the results of Anderson's discriminant analysis, the data were subjected to quadratic discriminant analysis, however this presented lower representation than the linear one, present in the article. Therefore, the authors decided not to present these results in the manuscript.

Attachment Submitted filename: Response to Reviewers.docx

10.1371/journal.pone.0305610.r003
Decision Letter 1
Silva Claudionor Ribeiro da Academic Editor
© 2024 Claudionor Ribeiro da Silva
2024
Claudionor Ribeiro da Silva
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
4 Jun 2024

Spectral indices with different spatial resolutions in recognizing soybean phenology

PONE-D-24-03115R1

Dear Dr. Silva,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Reviewer #1: "Differentiation"?

In table 3 and other places, is it not better to just say, "Normalized Difference Vegetation Index"?

I think you should deal with this word: "Differentiation"

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10.1371/journal.pone.0305610.r004
Acceptance letter
Silva Claudionor Ribeiro da Academic Editor
© 2024 Claudionor Ribeiro da Silva
2024
Claudionor Ribeiro da Silva
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
20 Aug 2024

PONE-D-24-03115R1

PLOS ONE

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on behalf of

Dr. Claudionor Ribeiro da Silva

Academic Editor

PLOS ONE
==== Refs
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