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Type of the Paper (Communication.) 
Prediction of biometric variables through multispectral images 
obtained from UAV in beans (Phaseolus vulgaris L.) during rip-
ening stage 
Javier Quille-Mamani 1*, Rossana Porras-Jorge 1, David Saravia-Navarro 1, 2, Jordán Herrera1, Julio Chavez-Galarza 1, 
Carlos I. Arbizu 1 
1 Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Mo-
lina 1981, Lima 15024, Lima, Perú; R. P-J. (riego_tecnificado@inia.gob.pe); D.S.-N. (agricultura_preci-
sion@inia.gob.pe); J.H. (jordanhf13@gmail.com); J. Ch-G. (jcchavezgalarza@gmail.com); C.I.A. (car-
bizu@inia.gob.pe) 
2 Facultad de Agronomía, Universidad Nacional Agraria La Molina, Av. La Molina s/n, Lima 15024, Lima, 
Lima Perú. 
* Correspondence: geomatica@inia.gob.pe; +51 945189689 
Abstract: Here, we report the prediction of vegetative stages variables of canary bean crop by means 
of RGB and multispectral images obtained from UAV during the ripening stage, correlating the 
vegetation indices with biometric variables measured manually in the field. Results indicated a 
highly significant correlation of plant height with eight RGB image vegetation indices for the canary 
bean crop, which were used for predictive models, obtaining a maximum correlation of R2 = 0.79. 
On the other hand, the estimated indices of multispectral images did not show significant correla-
tions. 
Keywords: Vegetation indices; precision agriculture; RGB images 
 
1. Introduction 
Bean (Phaseolus vulgaris L.) is a legume with a protein content of 20 to 25% and 50 to 
60% carbohydrates, and is part of the human diet worldwide, mainly in developing coun-
tries [1]. It is widely cultivated for its enormous genetic diversity, it is a nitrogen-fixing 
plant, highly adaptable and productive in a wide range of environments [2, 3]. This crop 
will likely play a key role in guaranteeing food security for millions of people around the 
world in the near future [4]. In Peru in 2019, a total of 73,298 ha of beans were cultivated, 
representing 0.8% of the Gross Value of Agricultural Production (GVaP) [5]. Canary beans 
is the most outstanding cultivar in the Peruvian coast due to its preference in the national 
diet, and cultural aspects. Therefore, due to the importance of this crop, it is a great chal-
lenge to monitor its development together with an appropriate agronomic management 
in the field [6] in the context of climate change. 
Quantitative evaluations of biometric variables such as plant height, leaf area index, 
and chlorophyll content influence yield and are becoming a high priority under precision 
agriculture [7]. Efficient and non-destructive monitoring of crop growth is essential for 
accurate crop management and is key to digital agriculture [8]. Determining data manu-
ally requires a significant amount of time and resources (measuring equipment, reagents, 
and researchers, among others). To increase agricultural production with limited re-
sources, important technological advances have been implemented such as the use of un-
manned aerial vehicles (UAVs) [9]. 
UAVs are tools that provide new alternatives of monitoring crops, without direct 
contact on them [10], allowing the prediction of crop development in a spatial-temporal 
way. The sensors coupled to a UAV allow estimating vegetative development variables 
©  2021 by the author(s). Distributed under a Creative Commons CC BY license.
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with different vegetative indices [11]. Recently, in the north of China, [12] used UAV im-
ages to predict yield in corn crop. Similarly, other researchers, predicted aerial biomass in 
various crops such as sunflower, corn and wheat [13–16]. Similarly, in Kenya RGB images 
were used to estimate growth and nutritional yield of cassava [17]. Since evaluations are 
necessary during the development of crops and as beans are one of the main foods in the 
basic family basket, it is essential that research is conducted to guarantee food security 
under the context of climate change. Therefore, the objective of the present work was to 
estimate the prediction of plant height, chlorophyll content and leaf area index through 
multispectral images obtained from UAV of canary bean crop during the ripening stage. 
 
2. Materials and Methods 
2.1. Study site 
The study site is located in the research field of the National Institute of Agrarian 
Innovation (INIA for its acronym in Spanish) (12°4ʹ30.10ʹʹS, 76 ° 56ʹ33.86ʹʹW, 240 masl) 
(Figure 1). The experiment was developed during the winter-spring seasons (Jun 26-Oct 
20, 2020), using commercial canary beans. This research was conducted in an experimental 
field of 0.30 ha (Figure 2) with distances between plants and rows of 0.2 m and 0.9 m, 
respectively. We used drip irrigation with a distance between drippers of 0.2 m with a 
flow of 1.20 l/h. The study site is arid according to the climatic classification of Warren 
Thornthwaitees [18], recording for the year 2020 averages of 76.8% RH, wind speed 3.3m/s 
and temperature 19.2 °C (Figure 1), and a total annual rainfall of 8 mm. The meteorological 
data was recorded from an automatic station (VANTAGE Pro2 Plus Davis, California, 
USA), located at the Alexander Von Humbolt meteorological observatory of the La Molina 
National Agrarian University at a distance of 0.87 km from the research area. 
2.2. Field data collection 
Three biometric variables were recorded on 16 plots of 7m x 5m at 90, 97 and 101 
days after sowing (DAS): 
• Plant height (cm); it was measured manually from the soil surface to the highest stem 
apex. 
• Leaf area index (LAI); it was estimated from digital images taken on the canopy cover 
of the plant on a 0.2m x 0.9m frame. Image processing was performed with the Green 
CropTracker software (v. 1.0, Agriculture and Agri-Food Canada) using a histogram-
based threshold method according to [19]. 
• Chlorophyll content (mg / m2); it was measured with the CCM-300 equipment (Opti-
Sciences Hudson, NH, USA) following the methodologies of [20]  choosing leaves 
from the upper third free of mechanical and biotic damage. 
 
2.3. UAV RGB and multispectral image acquisition and processing 
 
We used a Quadcopter type UAV platform, DJI Phantom 4 Pro (Shenzhen Dajiang 
Baiwang Technology Co., Ltd., Shenzhen, China) with a built-in 4864 × 3648 pixel resolu-
tion RGB camera. In addition, a multispectral sensor Parrot Sequoia (Parrot SA, France) 
which is a synchronized array of 4 single-band multispectral camera with 1.2 MP global 
shutter was attached, taking images in green (550 nm), red (660 nm), red edge (735 nm) 
and near infrared (790 nm). 
The images were collected at 90, 97 and 101 DAS during sunny days with wind 
speeds lower than 12 m/s, from 11:00 to 13:00 hours approximately on the 16 research 
plots, as detailed in Figure 2. The flight plan was established with the Pix4Dcapture soft-
ware (v. 4.12.1, Pix4D SA, Prilly, Switzerland), considering a frontal and lateral overlap of 
 
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80%, height of 30 m, speed of 2.8 m/s and the camera focused at nadir position (perpen-
dicular to the ground surface), allowing to obtain a resolution of 0.8 cm and 2.5 cm for 
RGB and multispectral images, respectively.  
Figure 3 shows a flow chart for data acquisition from UAV platform to image pro-
cessing. Image processing was performed with Pix4Dmapper Pro software (v. 4.3.33, 
Pix4D SA, Prilly, Switzerland), according to the following steps: (i) alignment of geolo-
cated images, (ii) generation of point clouds and geometric correction, and (iii) generation 
of the digital surface and orthomosaic model using the inverse distance weighting 
method. The geometric correction was performed considering nine ground control points 
(GCP) (Figure 2) previously installed and registered with differential GNSS (Global Nav-
igation Satellite System) (South Galaxy G1 model, South Surveying & Mapping Instru-
ment Co. Ltd, Guangdong, China). 
 
2.4. Calculation of vegetation indices 
 
The calculation of the vegetation indices from RGB images was carried out after to a 
normalization of the bands (R = red, G = green and B = blue) in the Pix4Dmapper software. 
In addition, the multispectral bands were obtained (R = red, G = green, RE = red border 
and NIR = near infrared). In this research, 19 vegetation indices were estimated to assess 
biometric parameters (Table 1) 
 
2.5. Statistical analysis 
 
We recorded the quantitative data (plant height, leaf area index and chlorophyll con-
tent) in a field book for the 16 plots and were then analyzed with the R v software. 4.1.0 
[21] with the following packages: GGALLI v. 2.0.0 [22], Hmisc v. 4.5-0 [23]. In addition, 
we employed R base codes to correlate variables evaluated in the field with those esti-
mated through vegetation indices. Statistical indicators such as the Pearson correlation 
coefficient (r) and determination coefficient (R2) were determined. Moreover, a principal 
component analysis was performed with libraries factoextra v.1.0.7 [24] and FactoMiner 
v.2.4 [25]. 
3. Results 
3.1. Principal component analysis 
 
The principal component analysis (PCA) (Figure 4) defined the interactions between 
the variables evaluated in the field (plant height, leaf area index and chlorine-row con-
tent.) with the vegetation indices (19 in total) monitored by UAV with a cumulative vari-
ance of 80.6% for the first two dimensions. In addition, PCA generated differentiated clus-
ters between 90 and 97 DAS. However, the last evaluation (101 DAS) did not report a 
significant difference, indicating that during ripening stage, these variables in the bean 
crop show minimal rates of increase. 
The PCA variables on the axis of dimension 1 that contribute to the variance repre-
sent an approximate of 5% in each one. We can also observe that the multispectral indices 
were greater at 90 DAS as well as the chlorophyll content (Figure 4). 
3.2. Correlation between spectral indices and growth variables 
The correlation analysis between the vegetation indices and growth variables were 
carried out for 90, 97 and 101 DAS, finding significant correlations (p-value <0.05) at 97 
DAS for plant height. In contrast, the other variables (chlorophyll content and leaf area 
index) did not register significant correlations, so we decided not to consider them for the 
multiple linear regression model. 
Plant height is an important variable since it depends on the growth rate of crops 
[26]. In Table 2, 19 vegetation indices correlated to plant height are detailed. Four indices 
(RGBVI, VDVI, ExGR and ExB) presented high significance (p-value <0.01), eight indices 
 
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(ExR, GBDI, NGBDI, NGRDI, MGRVI, VARI, CRRI and RGRI) showed significance (p-
Value <0.05), and four indices (ExB, ExR, IKAW and ExG) were inversely proportional. Of 
the total of evaluated indices, no multispectral index presented significant correlations 
(NDVI, SAVI, GNDVI and NDREI). 
3.2. Prediction model for estimating plant height 
After correlating the vegetation indices and plant height at 97 DAS, multiple linear 
regression analyzes were performed for three prediction models, as detailed in Table 3. 
Figure 5 shows the three prediction models for the measured plant height and its pre-
dicted values: (i) for model I, 10 vegetation indices were used (RGBVI, GBDI, VDVI, 
NGBDI, NGRDI, MGRVI, VARI, ExGR, CRRI, RGRI) with significant correlations 0.59 < r 
<0.64, (p-value <0.05), generating a predictive model with an R2 = 0.79 (p-value <0.01), (ii) in 
model II four vegetation indices were considered (RGBVI, GBDI, VDVI, ExGR) with sig-
nificant correlations of 0.63 <r <0.64, (p-value <0.01), obtaining a predictive model with R2 
= 0.60, and (iii) to generate the prediction model III, three vegetation indices were consid-
ered ( ExR, ExG, ExB) with negative correlations -0.66 <r<-0.32, generating a predictive 
model with an R2 = 0.46. Although for model II four vegetation indices with high correla-
tion values were chosen, their prediction decreases due to the fact that it presents an R2 
lower than model I but higher than that of model III. 
3.3. Figures, Tables and Schemes 
 
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Figure 1. Meteorological data for year 2020 recorded hourly. A) temperature (°C), B) relative hu-
midity (%) and C) wind speed (m / s). The blue line represents the daily average. 
 
 
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Figure 2. Location of the 64 canary type bean cultivation plots. The ground control points (GCP) 
are shown, and the 16 plots sampled for the estimation of vegetation indices are shown in blue 
rectangles. 
 
Figure 3. Flowchart for the acquisition and processing of images for the calculation of the vegeta-
tion indices of a canary type bean crop. 
 
Figure 4. Principal component analysis of growth variables and vegetative indices during ripening 
stage of canary type bean crop. 
 
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Figure 5. Correlation between the estimated data and plant height recorded for three models, A) 
Model I is based on ten vegetation indices, B) Model II is based on four vegetation indices, and C) 
Model III is based on three vegetation indices with inverse correlation. 
 
Table 1. Vegetation index calculations from Red-Green-Blue and Multispectral images 
Abbrev  Definition  type Reference 
NGRDI (G - R)/(G + R) [27, 28] 
IKAW (R − B)/(R + B) [28] 
ExR 1.4xR− G [29] 
ExB 1.4xB − G [29, 30, 31] 
ExG 2xG-R-B [32] 
RGB 
CRRI G/R [32, 28] 
ExGR ExG − ExR [20] 
GBDI G-B [33] 
MGRVI (G x G-R x R)/(G x G x R x R) [34] 
NGBDI (G - B)/(G + B) [35, 36] 
 
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(G x G - B x R)/(G x G + B x 
RGBVI [36, 37] 
R) 
VDVI (2 x G - R -B)/(2 x G + R +B) [27] 
RGRI R/G [38] 
VARI (G − R)/(G + R − B) [39] 
(0.441 x R - 0.8818 xG + 0.385 
CIVE [35] 
x B + 18.787) 
NDVI (NIR − r)/(NIR + r) [40] 
GNDVI (NIR − g)/(NIR + g) Multiespectral [41] 
SAVI [(NIR − r)/(NIR + r+L)](1+L) [42] 
NDREI (NIR − re)/(NIR + re)   [43] 
 
Table 2. Vegetation index correlated with plant height recorded at 97 DAS. 
Index  Plant height 
   r Pearson p-value 
 
NDVI 0.39 0.135 ns 
SAVI 0.40 0.125 ns 
GNDVI 0.33 0.215 ns 
NDREI 0.37 0.156 ns 
CIVE 0.22 0.418 ns 
RGBVI 0.64 0.008 ** 
ExR -0.62 0.011 * 
GBDI 0.63 0.010 * 
VDVI 0.64 0.007 ** 
NGBDI 0.60 0.014 * 
NGRDI 0.61 0.011 * 
MGRVI 0.61 0.012 * 
IKAW -0.18 0.502 ns 
VARI 0.59 0.017 * 
ExGR 0.63 0.009 ** 
ExG -0.32 0.223 ns 
CRRI 0.62 0.010 * 
RGRI 0.60 0.014 * 
ExB -0.66 0.005 ** 
 
Table 3. Multivariate linear regression models between vegetation indices and plant height rec-
orded during ripening stage for canary bean. 
Prediction Spectral Regression 
Intercept Model R2 
model Index Coefficient 
I RGBVI -2893.1 5934.9  0.79 
 
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GBDI 21228.7 
VDVI 5500.7 
NGBDI - 8715.3 
NGRDI 5934.9 
MGRVI -9715.4 
VARI 3526.0 
EXGR - 5071.7 
CRRI - 139.1 
RGRI 1657.0 
RGBVI - 3556 
GBDI 4832 
II 2709 0.60 
VDVI -1332 
EXGR - 3179 
EXR -91.31 
III EXG 210 51.45 0.46 
EXB - 216.05 
 
4. Discussion and conclusions 
To our best knowledge, this is the first research work in Peru using technology such 
as UAV for an agronomic monitoring of canary beans. A highly significant correlation for 
plant height was obtained from eight estimated vegetation indices from RGB images in 
the canary bean crop at ripening stage. It was possible to predict the height of this crop 
with high performance (R2 = 0.79) using model I. Recent studies [2, 17] reported similar 
results for beans and cassava crops, respectively, demonstrating the utility of UAVs and 
vegetation indices for the compilation of biometric variables. The use of these tools be-
comes more important due to the need to conduct more agronomic evaluations in a timely 
manner in crops and mainly in those that are cultivated in arid environments such as the 
Peruvian coast. We are currently conducting experiments with new technologies using 
remote sensors such as UAV for the prediction of biometric variables estimated from veg-
etative indices obtained from RGB and multispectral images in bean crops and others of 
national importance. In addition, images from the PeruSat-1 (Peru) and Kompsat 3 (South 
Korea) satellites will be used, allowing timely monitoring and prediction of crop develop-
ment under different agronomic management scenarios (irrigation, fertilization, among 
others) on a larger scale. 
 
Author Contributions: Conceptualization and methodology, J.Q.-M.; data analysis and methodol-
ogy, D.S.-N.; R. P.-J.; writing and revision, D.S.-N.; J. Ch.-G.; C.I.A; J.H. 
Funding: PIP 2281955 "Installation of the agricultural technology research service specialized in cli-
mate change for the agricultural sector" and PIP 2449640 "Creation of the precision agriculture ser-
vice in the departments of Lambayeque, Huancavelica, Ucayali and San Martín 4 Departments" 
Acknowledgments: We are grateful to PIPs 2281955 and 2449640 for providing funding. Likewise, 
thanks to the students of the Agrotechnology Research Circle (CIATEC for its acronym in Spanish) 
of the Faculty of Agronomy, La Molina National Agrarian University for their support during field 
evaluations. 
Conflicts of Interest: The authors declare no conflict of interest 
 
 
 
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