Aerial Monitoring of Rice Crop Variables using an UAV Robotic System

C. Devia

, J. Rojas

, E. Petro

, C. Martinez

, I. Mondragon

, D. Patino

, C. Rebolledo

2,3

and J. Colorado

School of Engineering, Pontiﬁcia Universidad Javeriana, Bogota, Colombia

The International Center for Tropical Agriculture -CIAT, Agrodiversity, Palmira, Colombia

CIRAD, AGAP-Pam, Montpellier, France

Keywords:

UAV, Precision Agriculture, Image Processing, Vegetative Indices, Multispectral Imagery, Machine Learning.

Abstract:

This paper presents the integration of an UAV for the autonomous monitoring of rice crops. The system

integrates image processing and machine learning algorithms to analyze multispectral aerial imagery. Our

approach calculates 8 vegetation indices from the images at each stage of rice growth: vegetative, reproductive

and ripening. Multivariable regressions and artiﬁcial neural networks have been implemented to model the

relationship of these vegetation indices against two crop variables: biomass accumulation and leaf nitrogen

concentration. Comprehensive experimental tests have been conducted to validate the setup. The results

indicate that our system is capable of estimating biomass and nitrogen with an average correlation of 80% and

78% respectively.

1 INTRODUCTION

The use of Unmanned Aerial Vehicles (UAV) in the

solution of agriculture-related problems spans a wide

range of operations such as crop variable measure-

ment (Gevaert et al., 2015),(Gago et al., 2015) fruit

detection (Carrijo et al., 2017), crop plot detection

(Hongli et al., 2017), crop yield improvement (Arroyo

et al., 2017) and crop mapping (Guo et al., 2012),

(Khanna et al., 2015). In (Dongyan Zhang, Xin-

gen Zhou, Jian Zhang, Linsheng Huang and Zhao,

2017) the sheath blight fungus disease of rice was de-

tected using a Phantom 2 UAV coupled with a high-

resolution RGB multispectral camera (Micasense).

Pix4D software was used to generate 2D and 3D geo-

referenced maps and to compute different vegetation

indices. In (Yong et al., 2016), (Lu et al., 2015),

UAVs were used for nitrogen estimation and chloro-

phyll quantiﬁcation (Uto et al., 2013) using hyper-

spectral sensors.

Multispectral images have been used in plant anal-

ysis for some time now (Naito et al., 2017). Initially,

few vegetation indices were commonly used, mainly

the Normalized Difference Vegetation Index (NDVI)

for estimating above ground biomass. However, due

to the presence of external factors such as water, soil

backgrounds and the difference of the crop at each

stage of growth, different vegetative indices were in-

troduced, such as the Soil-Adjusted Vegetation Index

(SAVI), the Modiﬁed SAVI (MSAVI), the Modiﬁed

Chlorophyll Absorption Ratio Index (MCARI) and

the Modiﬁed Triangular Vegetation Index (MTVI),

among others (Gnyp et al., 2014).

The development of an UAV-based crop monitor-

ing system involves multiple challenges. In hardware

terms the UAV requires sufﬁcient autonomy to cover

the crop area, also adequate on-board store capac-

ity for high-resolution data, as well as low-weight

sensors. The integration of the native UAV hard-

ware with external sensors requires a signiﬁcant in-

terplay between hardware and software in order to

guarantee data reliability, online processing and sim-

ple end-used experience. In previous work (Devia

et al., 2019), we tackled these challenges by devel-

oping an autonomous UAV robotic system to monitor

biomass dynamics based on NIR information. In this

paper, we present a preliminary approach to also mon-

itor leaf nitrogen concentration during the three main

stages of plant growth: vegetative, reproductive and

ripening. Our goal is to estimate biomass dynamics

and nitrogen variations by calculating several vegeta-

tion indices from multispectral data. To this purpose,

we present the robotic system architecture detailed in

Figure 1. Two UAVs have been setup with both NIR

and thermal cameras (upcoming work is oriented to-

wards cooperative-UAV monitoring). For instance,

Devia, C., Rojas, J., Petro, E., Martinez, C., Mondragon, I., Patino, D., Rebolledo, C. and Colorado, J.

Aerial Monitoring of Rice Crop Variables using an UAV Robotic System.

DOI: 10.5220/0007909900970103

In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019), pages 97-103

ISBN: 978-989-758-380-3

each robot captures a dataset of images independently

and the processing is done in a PC base-station.

2 METHODS

2.1 UAV Robotic System

As mentioned, two UAVs were integrated: The AS-

CTEC Pelican

and the DJI Phantom

. Also, the fol-

lowing equipment was used:

• ASCTEC Pelican:

– ASCTEC autopilot.

– Mastermind board for image processing and

data acquisition.

– Tetracam ADC-Lite multispectral camera: this

camera captures visible light wavelengths

longer than 520nm and near-infrared (NIR)

wavelengths up to 920nm. The multispectral

camera is located in the bottom of the drone and

aligned with the center of mass.

• DJI Phantom:

– DJI autopilot.

– Parrot Sequoia multispectral camera

: his cam-

era comes with 4 different sensor to capture im-

age in different wavelengths: red, green, NIR

and Red-Edge.

– Flir duo R thermal camera

with 640 × 512 in

resolution with 32

C ﬁeld of view. It incor-

porates a radiometric sensor operating at sam-

pling frequency of 30Hz and capable of sensing

the crop temperature between −20

C and 50

with a resolution of ±5

The standard GPS waypoint navigation for both

drones comes with a graphical user interface -GUI

that allows for autonomous take-off, waypoint tra-

jectory following and landing. In this work, the

ﬂight planning was enhanced with an additional mod-

ule created for the image capturing planning process.

This new module creates a GPS grid with the posi-

tions were the images must be taken in order to ensure

appropriate image resolution, crop plot coverage and

sufﬁcient overlapping of images for ensuring reliable

data-mapping of the crop. In this sense. our photo

planner algorithm uses geometric parameters of the

http://www.asctec.de/en/uav-uas-drones-rpas-roav/

asctec-pelican/

https://www.dji.com/phantom-4

https://www.parrot.com

https://www.ﬂir.com/products/duo/

NIR/thermal camera and the crop plot area to gener-

ate the cartesian points.

The required UAV altitude is also estimated in the

photo planning algorithm. This is done by using the

camera’s ﬁeld of view in each axis and the desired

image resolution. Figure 2 presents experimental re-

sults regarding aerial crop coverage and geo-mapping

of crop plots by applying classical image mosaicing

techniques. The aforementioned methods were pre-

sented in previous works reported in (Rojas et al.,

2017), (Jose et al., 2016), and (Rojas et al., 2018).

2.2 Crop Variable Estimation

This section addresses the challenges associated with

NIR and thermal image processing to properly com-

pute Vegetative Indices (VI). It mainly consists in

three different stages:

1. Image processing: By taking raw images from

the NIR camera, the system determines the crop

area (plots) that are suitable for VI analysis. In

this process we apply perspective corrections to

the images (using drone’s IMU information) and

ﬁltering strategies to remove the background and

noise. Finally, NIR image segmentation is applied

to extract the crop plot (parcel) of interest, as de-

tailed by Figure 3(a).

2. Vegetation index computation: VI are well-known

formulas that use the reﬂectance of the plants

in different wavelengths to provide information

about the health state of the plant. Table 1 details

the VI used in this work and their corresponding

formulas.

3. Machine Learning: Once the vegetative indices

are computed, we applied Multivariable Regres-

sion models (MR) to calculate the accumulated

biomass. Only polynomial models were consid-

ered using the vegetative indices as the indepen-

dent variables. Furthermore, we also used Artiﬁ-

cial Neural Networks (ANN) to estimate leaf ni-

trogen concentration by training our system dur-

ing the three main stage of rice growth: vegeta-

tive, reproductive and ripening. Both MR an ANN

models required a ground-truth dataset for train-

ing. In this sense, biomass and nitrogen were di-

rectly measured from the rice crop by following

the traditional sampling method. For biomass, 1

linear meter of plants were cut from the ground,

as detailed by Figure 3(b). Plants were sampled

and weighted, then put in the oven at 65 degrees

Celsius for 4 days or until a constant weight was

reached. For nitrogen, we used a SPAD sen-

sor (Soil-Plant Analyses Development) to directly

measured leaf nitrogen.

ICINCO 2019 - 16th International Conference on Informatics in Control, Automation and Robotics

WiFi

Base Station

drivers

database

User

DGPS

GPS+IMU

NIR image

Radiation

Temperature

Sensor

Fusion

MACHINE

LEARNING

Mission Setup Drone

commands

Biomass

Nitrogen

Water Stess

Rice Crop

Camera Setup

Thermal image

[ data ]

xbee

2.4GHz

NIR and thermal imagery

Camera

GPR

Drone 1

Drone 2

Figure 1: Multi-UAV robotic system architecture for crop monitoring.

Figure 2: (a) image mosaicing ﬂow diagram: SURF and

ORB algorithms were implemented for feature extraction.

The FLANN Fast (Library for Approximate Nearest Neigh-

bors) algorithm was used for matching, whereas RANSAC

(Random Sample Consensus) algorithm was used to cre-

ate the homography for coordinate frame transformations.

(b) Geo-referenced mosaicing results of applying the steps

from (a) to both NIR and RGB images. (c) resulting NIR

digital map of the crop and the trajectory followed by the

drone.

3 EXPERIMENTAL RESULTS

Experiments were carried out during 2017 and 2018

in the rice farms of the Center of International

Agriculture -CIAT, located in the Department of

Meta, Colombia: latitude 4

37.85

N with Longi-

tude 73

28.65

W . Figure 4 shows experimental

results for several weeks of in-ﬁeld testing. Both

MR and ANN methods have been used for the esti-

mation of the crop variables based on NIR imagery.

On average, the former, denoted as Estimation 1,

Table 1: NIR Vegetation Indices used (ρ

denotes the re-

ﬂectance of the for the frequency f ) (Gnyp et al., 2014).

Name Equation

Normalized

Difference Veg-

etation Index

-NDVI

780

−ρ

670

780

+ρ

670

Green Normal-

ized Difference

Vegetation

Index -GNDVI

780

−ρ

500

780

+ρ

500

Soil-Adjusted

Vegetation

Index -SAVI

(1 + L)



800

−ρ

670

800

+ρ

670



with L = 0.5

Modiﬁed SAVI

-MSAVI



2ρ

800

+ 1 −

(2ρ

800

+ 1)

− 8(ρ

800

− ρ

670

)



Corrected

Transformed

Vegetation

Index - CTVI

NDVI+0.5

|NDVI+0.5|

|NDVI + 0.5|

Difference Veg-

etation Index -

DVI

780

− ρ

670

achieves more accurate estimation compared against

the ground-truth value, denoted as Measurement. The

results are consistent for the three stages evaluated:

vegetative, reproductive and ripening. The left plots

of Figure 4 show the biomass and nitrogen estimation

values respectively. Abrupt variations of the signals

(e.g. sampled image 225 in the horizontal axis) indi-

cates a change of crop stage from vegetative to repro-

ductive. In this sense, the time evolution window of

both crop variables is about 3 months.

Using the VIs and the metadata of the corre-

sponding images, the multilinear regression models

Aerial Monitoring of Rice Crop Variables using an UAV Robotic System

10Please give a shorter version with: \authorrunning and \titlerunning prior to \maketitle

(a) NDVI (b) GNDVI (c) DVI (d) TVI (e) MSAVI

Fig. 3 Vegeta tive indic es f or upland r ice syste m duri ng ripen ing stage .

(a) NDVI (b) GNDVI (c) DVI (d) TVI (e) MSAVI

Fig. 4 Vegeta tive indic es f or lowland r ice syste m duri ng ve getat ive s tage.

viding mor e parameters with the same available information. Commonly, the

accumulation of biomass i n rice crops during the ripening stage behaves lin -

early with certain vegetation indices, however, for other stages of the crop, the

relationship between biomass and the vegetation indices could be nonlinear.

(a) Upland rice system (b) Lowland rice system

Fig. 5 Rice production systems: lowland and upland. Both pictures correspond to the crops

assessed during the experiments reported in this paper.

3 Field Report

Experiments were carried out during 2017 in the experimental station of CIAT

located in Santa Rosa-Meta (upland conditions) and Palmira-Valle del cauca

(lowland conditions) . The results of this ﬁeld report were obtained from three

NIR original image

Image Transformation Filtered image k-means clustering

Segmentation

8.1m

10m

4.2m

0.5m

Sampled plants

Edge

(a)

(b)

VI computation

Ground-truth for Machine Learning

Figure 3: (a) Experimental results regarding NIR image pre-processing algorithms. Classical image clustering based on k-

means has enabled to separate NIR pixels into two cluster: plant and no-plant. This allows the image background subtraction

to properly extract the parcel of interest. (b) Ground-truth dataset for machine learning training.

Table 2: Numerical values for biomass and nitrogen/SPAD in vegetative stage directly measured from the crop (ground-truth

values).

Plot/parcel Repetition Fresh weight [g] Dry weight [g] Water Content [g] SPAD/nitrogen

13B1 1 656 118 82.01 47.70

38B4 1 394.00 76.00 80.71 45.30

63B1 1 450.00 88.00 80.44 34.27

88B4 1 814.00 100.00 87.71 41.23

13B5 2 792.00 136.00 82.83 44.37

38B8 2 420.00 76.00 81.90 46.53

63B5 2 646.00 120.00 81.42 43.07

88B8 2 550.00 104.00 81.09 40.30

13B9 3 626.00 126.00 79.87 36.63

38B12 3 656.00 110.00 83.23 44.10

63B9 3 428.00 76.00 82.24 38.13

88B12 3 540.00 108.00 80.00 43.63

were performed for every possible combination of

vegetative indices. Strong linear dependencies were

found between the VIs from Table 1 and the dynamics

of biomass and nitrogen. For instance, linear multi-

variable regressions of the form: β

+ β

SRα

NDVI

NDVIα

NDV I

+ β

GNDVI

GNDVIα

GNDV I

CTVI

CTVIα

CTV I

+ β

SAVI

SAVIα

SAV I

DVI

DVIα

DV I

+ β

MSAVI

MSAVIα

MSAV I

were used to

estimate both crop variables, computing the 7 VIs

with a constant coefﬁcient, where α

, α

NDV I

GNDV I

, α

CTV I

, α

SAV I

, α

DV I

and α

MSAV I

take the

value of 0 or 1. For each coefﬁcient combination

(VIs and the constant term), two images were taken

randomly from each plot. This was done since not all

the plots had the same number of photos, so if all the

photos were used, the resulting regression could have

some bias.

For each stage of the crop (vegetative, reproduc-

tive and ripening), we performed 20 ﬂights, capturing

around 2, 000 images per stage, yielding a dataset of

6, 000 images per trial. Overall, around 18, 000 im-

ages were processed for the estimation of biomass

and nitrogen. Both MR and ANN methods used a

training set of NIR images accounting for the 60% of

the entire database, whereas the remaining 40% for

testing and validation. On the other hand, the corre-

lations between estimated data and the ground-truth

measurements are shown in the histograms depicted

in the right plots of Figure 4. On average, our system

is capable of estimating biomass and nitrogen with a

correlation of 80% and 78% respectively

. Higher

correlations above 80% were achieved during the ﬁrst

vegetative stage of the crop. During this stage, the

The following video illustrates the steps performed dur-

ing the experiments: https://youtu.be/BTwD4GduXDo

ICINCO 2019 - 16th International Conference on Informatics in Control, Automation and Robotics

100

0 50 100 150 200 250 300 350 400 450 500

Sample Images

200

400

600

800

1000

1200

1400

1600

1800

2000

Biomass

Estimation (Cir) - Vegetative Stage

Estimation (Seg) - Vegetative Stage

Measurement - Vegetative Stage

Estimation (Cir) - Reproductive Stage

Estimation (Seg) - Reproductive Stage

Measurement - Reproductive Stage

Estimation (Cir) - Ripening Stage

Estimation (Seg) - Ripening Stage

Measurement - Ripening Stage

0 50 100 150 200 250 300 350 400 450 500

Sample Images

SPAD

Estimation (Cir) - Vegetative Stage

Estimation (Seg) - Vegetative Stage

Measurement - Vegetative Stage

Estimation (Cir) - Reproductive Stage

Estimation (Seg) - Reproductive Stage

Measurement - Reproductive Stage

Estimation (Cir) - Ripening Stage

Estimation (Seg) - Ripening Stage

Measurement - Ripening Stage

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Correlations

Histogram of Correlations BM

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Correlations

Histogram of Correlations Nt

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Correlations

100

Histogram of Correlations BM

-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Correlations

Histogram of Correlations Nt

Vegetative stage

Biomass Correlations

Ripening Stage

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

100

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

2000

1800

1600

1400

1200

1000

800

600

400

200

Biomass

0 50 100 150 200 250 300 350 400 450 500

Sampled images

Correlations

SPAD (Nitrogen)

0 50 100 150 200 250 300 350 400 450 500

Sampled images

(a)

(b)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Nitrogen Correlation

Estimation 1 - Vegetative stage

Estimation 2 - Vegetative stage

Measurement - Vegetative stage

Estimation 1 - Reproductive stage

Estimation 2 - Reproductive stage

Measurement - Reproductive stage

Estimation 1 - Ripening stage

Estimation 2 - Ripening stage

Measurement - Ripening stage

Ripening Stage

Estimation 1 - Vegetative stage

Estimation 2 - Vegetative stage

Measurement - Vegetative stage

Estimation 1 - Reproductive stage

Estimation 2 - Reproductive stage

Measurement - Reproductive stage

Estimation 1 - Ripening stage

Estimation 2 - Ripening stage

Measurement - Ripening stage

Vegetative stage

Ripening Stage

Correlations

Figure 4: Experimental results (a) Biomass estimation (dry weight), (b) Nitrogen estimation. Left plots compare the estimated

variables (labels Estimation 1 and 2) against the ground-truth labeled as Measurements. For nitrogen, this values is directly

measured using a SPAD device. The time evolution window of both crop variables is about 3 months: vegetative, reproductive

and ripening. Right plots contain the histogram information regarding the correlations achieved during the experiments.

green color of the plants is predominant, as observed

by the inset in Figure 4(a). Therefore, our methods

can be properly calibrated based on accurate VIs cal-

culated from NIR image reﬂectances. Numerical val-

ues of the ground-truth measurements for the vege-

tative stage are consigned in Table 2. The reproduc-

tive stage is the more critical for our estimations al-

gorithms since panicle formations yield yellow colors

in the images. The merge of both green and yellow

colors difﬁcult the training, specially during the clus-

tering phase where only two clusters are conformed:

plants (green pixels) and no-plant (soil) cf. Figure

3(a). Larger ﬂuctuations in biomass and nitrogen es-

timations occur in this crop stage, as observed in both

Figure 4(a)(b) left plots. Lastly, in ripening stage, the

yellow color becomes predominant, allowing a proper

calibration and therefore accurate estimation of the

crop variables.

4 CONCLUSIONS

This paper presented the integration and deployment

of an UAV system for rice crop monitoring. By using

multivariable regressions and neural networks, our

system was able to estimation biomass and nitrogen

dynamics during the time evolution of the crop, con-

cretely for three stages: vegetative, reproductive and

ripening. From the specialized literature, we identi-

ﬁed the set of vegetation indices that were sensible

to biomass and nitrogen variations according to NIR

image reﬂectance properties. We combined those in-

dices to achieve the estimations of both values. A

comprehensive ﬁeld testing of the proposed system

Aerial Monitoring of Rice Crop Variables using an UAV Robotic System

101

enabled us to calculate correlations between the es-

timations and the in-ﬁeld measurements of the crop

variables. Since the vegetation indices tend to be

evolve linearly during the crop growth, we achieved

accurate correlations using multivariable regressions;

on average, correlations of 80% for biomass and 78%

for nitrogen were achieved. Upcoming work is ori-

ented towards improving the correlations by including

more sophisticated image classiﬁcation and clustering

algorithms to consider several feature spaces for the

NIR pixels. By now, our system is not reliable during

the reproductive stage of the crop due to the mixed

plant color in between yellow and green. Also, differ-

ent genotypes of rice varieties are planted in the same

plot area. In this sense, we also expect to improve on

the estimation, since the biomass and nitrogen read-

ings are highly dependent of the plant variety.

ACKNOWLEDGEMENTS

This work was funded in part by the OMICAS pro-

gram: Optimizaci

on Multiescala In-silico de Cul-

tivos Agr

ıcolas Sostenibles (Infraestructura y vali-

daci

on en Arroz y Ca

na de Az

ucar), sponsored within

the Colombian Scientiﬁc Ecosystem by The WORLD

BANK, COLCIENCIAS, ICETEX, the Colombian

Ministry of Education and the Colombian Ministry

of Industry and Turism under GRANT ID: FP44842-

217-2018. Also, by the research project entitled De-

sarrollo de una herramienta para la agricultura de

precision en los cultivos de arroz: sensado del es-

tado de crecimiento y de nutricion de las plantas us-

ando un drone autonomo, under the COLCIENCIAS

- GRANT ID 120371551916, CT167-2016 (FONDO

NACIONAL DE FINANCIAMIENTO PARA LA

CIENCIA, LA TECNOLOGIA Y LA INNOVACION

-FRANCISCO JOSE DE CALDAS).

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