UAVs Team and Its Application in Agriculture: A Simulation

Environment

Floriano De Rango, Nunzia Palmieri, Mauro Tropea and Giuseppe Potrino

Dept. Computer Engineering, Modeling, Electronics and System Science, University of Calabria,

Ponte Pietro Bucci, Rende, Italy

Keywords:

UAVs Team, Simulation, Coordination.

Abstract:

The work proposes a simulation environment for UAVs management in the agriculture domain. In these last

years, new technologies can effectively support farmer to face issues and threats such as parasites and sudden

climatic changes that can severely degrade the crop or the quality of the cultivated products. However, to

properly manage a UAVs team, equipped with multiple sensors and actuators, it is necessary to test these

technologies in order to plan speciﬁc strategies and coordination techniques able to efﬁciently support farmers

and achieve the targets. At this purpose, this contribution proposes a simulator suitable for the agriculture

domain, where it is possible to set many parameters of this domain of interest.

1 INTRODUCTION

In this paper, it is supposed to use UAVs (also called

drones) in the agriculture domain in a situation where

it is necessary to monitor plants or products in a land.

Often, farmers need to face many issue such as par-

asites or sudden climate changes that can severely

affect the agricultural products quality. At this pur-

pose, new technologies such as drones equipped with

speciﬁc sensors, cameras and fertilizers can support

farmers to face the threats and, in the speciﬁc case, to

kill parasites that can destroy plants in the cultivated

ﬁeld (Zhang and Kovacs, 2012).

Let us suppose to have a cultivated area where a

certain number of plants such as trees are distributed.

In this area, it is possible that some parasites, in any

part of the area, can generate and attack the nearly

plants and to reproduce themselves and ﬁnally destroy

the plants in the area. In this case, a UAVs team can

be very useful in overseeing the overall area in order

to localize through cameras the attacked plants or to

see parasites moving among plants.

In order to propose coordination techniques of

drones for precision agriculture domain, it is neces-

sary to have a simulator where it is possible to set the

main parameters of the domain of interest and test the

proposed strategies and protocols for coordinating the

drones in the area.

There is no simulator till now, at the best of our

knowledge, that provide these modules and this tes-

tiﬁes the novelty of the research area although an in-

creasing interest has been shown on the industry and

research ﬁeld.

The paper is organized in the following way: in

Section 2 the related work about the coordination

techniques of swarm of drones is presented; the pro-

posed simulator and its parameters are presented in

Section 3. Section 4 shows the simulation environ-

ment with results and ﬁnally the conclusions are sum-

marized in Section 5.

2 RELATED WORK

Recent works have been proposed regarding the use

of drones or micro-drones in precision agriculture do-

main, showing as the applications of drones for preci-

sion agriculture can be interesting and very useful for

farmers especially if the technology can reduce the

cost of control systems (see (Costa and et.al, 2012);

(Primicerio and et al., 2012); (Zhang and Kovacs,

2012)). Moreover, through the introduction of spec-

trometry or cameras on drones, it is possible to evalu-

ate the height of the crops or soil moisture that can be

useful to understand how the crop is raising such as

shown in (Anthony and et.al, 2014); (Colomina and

Molina, 2014); (Hassan-Esfahani and et.al, 2014).

UAVs applications in agriculture are described in

(Pederi and Cheporniuk, 2015). The authors list some

ﬁelds of applications of UAV spraying different pro-

tection chemical like insecticide, fungicides and her-

bicides. Crops spraying UAVs are suited for these

tasks because many ﬁelds need of ultra low appli-

cation volume of pesticides per hectare and only on

some speciﬁc zones of a ﬁeld and only at a speciﬁc

374

Rango, F., Palmieri, N., Tropea, M. and Potrino, G.

UAVs Team and Its Application in Agriculture: A Simulation Environment.

DOI: 10.5220/0006466303740379

In Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2017), pages 374-379

ISBN: 978-989-758-265-3

time. In (Devraj and et.al, 2015) authors propose

a web-based system that facilitate farmers/extension

workers to diagnose insects/pests of major pulse crops

suggesting the suitable treatments.

In (Ye and et al., 2013) four architectures of Preci-

sion Agricultural Management Systems (PAMS) are

proposed. These architectures are: the spatial infor-

mation infrastructure platform, the IoT infrastructure

platform, the agriculture management platform and

the mobile client.

In order to support the study of drones, micro-

drones or UAV applied to precision agriculture, it is

necessary to design a simulator able to support in a

modular and ﬂexible way the different actors playing

in the domain. All these issues are focused at an early

stage in this work, showing as it is possible to model

each single module such as mobility module for par-

asites, mobility module for drones, resistance char-

acteristics of plants, and communication technologies

(De Rango et al., 2008), (Fotino et al., 2007) of drones

useful to implement a coordination strategy among

drones (Palmieri et al., 2017).

3 UAVS SIMULATOR

We consider a team of UAVs/drones, that performs a

mission of monitoring a land for destroying parasites

in the plants. Once the parasites are detected by a

UAV, it tries to destroy them through a certain quan-

tity of pesticide that the UAVs carry. However, the

pesticide resources of each drones are limited in quan-

tity. Once a drone ( called leader) notices parasites in

a region, it has to form a coalition based on its current

resource level such as pesticide and energy resources

and it broadcasts some information (i.e, its location,

type and number of its capabilities) to the other UAVs.

The other drones, that have the required resources,

will evaluate their availability to reach the leader or

not. It should be noticed that the coalitions formed are

temporary by nature; once the detected parasites are

destroyed, the coalition members can perform other

tasks.

Drones movement is modeled through the use of

a local map that is stored on-board. This map collects

info about already visited ﬁelds, if there are plants in

the near ﬁeld (cell) and if some plants are infected or

not. Each of these info last for a time on the basis of

the time set by the simulator. This temporal depen-

dence of plants and insects reﬂect the real situation

where an insect can attach again the plant or a plant

can be planted again on the ﬁeld.

The map is implemented by a cells square matrix and

each cell represents a portion of ﬁeld. The drone is

assumed to be in the middle of the cell. The info

related to the cell can be the presence or not of the

plant, the presence of parasites and consequently if

the plant can be infected or not. Moreover, the info

in the cell can also express if a plant is under care or

if the cell has not been explored yet. Drone updates

its map after any movement removing the old info on

the map and replacing them with more updated info.

This local map helps the drone about the next ﬁeld

(cell) to visit. It selects one of the unexplored cells

performs the ﬁeld selection. The cell selection prob-

ability is uniformly distributed among all unexplored

cells. However, after visiting some cells, the selec-

tion probability can be distributed again among the

remaining cells.

When all cells in the local map cannot be visited be-

cause they have been already visited from a few time

or because these cells are occupied by a health plant,

the cell selection probability can be changed.

Each drone can exchange its map with all neigh-

bor drones (drones within the drones transmission

range). This possibility can assure that a drone can

know the situation of neighbor cells also without go-

ing to explore them and this speed up the convergence

of the overall ﬁeld exploration.

Moreover, we consider many plants disseminated on

the ﬁeld that can attract the parasites. It is assumed

that each parasite does not communicate with other

parasites to perform a strategy but it moves on the ba-

sis of its local scope. It is oriented towards a single

direction and it is limited by its local scope that is as-

sumed in our case to be its three cells in front. During

the movement the parasite can go in one of the adja-

cent and visible cells.

4 SIMULATION ENVIRONMENT

4.1 Simulator Indicators in the GUI

Each drone is equipped with a battery providing the

energy to ﬂy and move in the interested area. It is

also equipped with a pesticide tank to spray on the

parasite to kill them and a wireless module to com-

municate with neighbor drones. The communication

range of each UAV is assumed to be limited. How-

ever, a UAV can reach another UAV by a sequence

of communication links. The fuel tank is considered

in milliliters (ml) whereas the battery power in Watts

(W). All these parameters are selected before assess-

ing the simulation through a GUI in the front-end of

the simulator.

The battery power can be dissipated in three ways:

drone movements, pesticide spraying and communi-

UAVs Team and Its Application in Agriculture: A Simulation Environment

375

Figure 1: Example of UAVs level.

cation, which is due to radiation, signal processing as

well as other circuitry. It is assumed that to eliminate

a parasite is necessary a ﬁxed amount of pesticide.

It is also assumed that drones can move at a con-

stant speed from a cell to another one or they can stop

to spray the pesticide on the tree attached by parasites.

This assumption could be removed extending the mo-

bility model and without affecting the validity of the

overall proposed simulator model.

Moreover, when the fuel tank is below a minimum

level the drone needs to come back at its base station

or when the drone terminated its pesticide. In order to

provide a friendly graphical interface, all drones are

represented with some indicators that can change the

color on the basis of the quantity of remaining pesti-

cide or the battery level such as shown in Fig.1.

In the Fig.1 it is shown the circle colored in the most

internal part to indicate the residual pesticide quantity

of each drone i (S

) using colors listed in Table 1. The

residual battery levels of each drone i (P

), instead, is

represented with colors indicated in Table 2.

Indicators are also related to the tree health in order

to represent the resistance level to the parasites attack

(Table 3). More speciﬁcally, when a tree has been

attached to parasites, its health decreases on the ba-

sis of number of parasites attaching the three and the

time of the parasites remain into the three. We refers

to H

as the current health state of each tree and H as

the initial state.

Table 1: Indicator of pesticide remaining quantity.

S (tank capacity)

S < S

≤

S (tank capacity)

0 < S

≤

S (tank capacity)

Table 2: Indicator of battery remaining quantity.

P (Initial battery capacity)

S < P

≤

P (Initial battery capacity)

0 < P

≤

P (Initial battery capacity)

Table 3: Tree health indicators.

H < H

≤

0 < H

≤

= 0 (Three completely damaged)

4.2 Simulator Front-end

In Fig.2 it is shown an example of the graphical inter-

face of the designed simulator. Fig. 3 shows the GUI

to set all simulation parameters. More speciﬁcally, the

parameters adopted in the simulation are listed in the

following:

• Pesticide tank: it indicates the capacity of the pes-

ticide tank (ml);

• Maximum battery level: it indicates the maximum

battery level when the battery is charged;

• Minimum battery level: it indicates the minimum

battery level to be preserved to reach the base sta-

tion;

• Percentage of trees in the ﬁeld: it represents the

tree percentage to distribute on the considered

ﬁeld;

• Tree lifetime: it is the initial health state of all

trees; it is represented by a number that can be

decreased if a parasite attaches the tree;

• Base station number: it represents the number of

base station from which drones can leave or can

come back to recharge the batteries;

• Energy spent in unit of pesticide spray: it indi-

cates how much energy is dissipated in spray 1 ml

of pesticide;

• Movement energy: it represents the energy dissi-

pated to move by 1 m in the considered area;

• Tree damage due to parasites (for each time unit):

it indicates the damage attributed to the tree by the

parasite for each time unit that parasite attaches

the plant; it is important because reduces the tree

health state and it can reduce the number of trees

on the ﬁeld;

• Minimum pesticide amount to kill a parasite: it in-

dicates the minimum amount of pesticide (in ml)

to kill a single parasite;

• Communication link bandwidth : it is the band-

width related to the communication link; it is im-

portant because it affects the transmission delay of

all info that a drone sends to other drones (for ex-

ample the local map distribution among neighbor

drones);

SIMULTECH 2017 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

376

• Simulation number: it is the number of indepen-

dent simulations before elaborating statistics; it

is important to present simulations results in the

conﬁdence interval of 95 % ; this parameter can

be selected at the beginning of the simulation and

it can change on the basis of the simulation pa-

rameters adopted;

• Communication distance: it is the transmission

range among drones expressed in meter;

• Pesticide spraying delay : it represents the time

necessary to perform 1 ml spray of pesticide;

• Recharge time: it indicates the minimum amount

of time to recharge the battery and reﬁll the pesti-

cide tank at the base station;

• Parasite movement delay : it is the time necessary

to the parasite to move by 1m;

• Map update time: it is the periodic time that

drones exchange their local map with other neigh-

bor drones; it is important because it can affect the

convergence time of the link state routing and it

can affect also the topological info of the drones

network

• Map lifetime: it is the time till MAP informa-

tion is considered valid by drone; it assured that

info needs to be updated otherwise they will be

removed because obsoleted; this assures also that

a drone can visit again a cell if it does not receive

any more info of other drones about the cell sta-

tus; this assured that if some parasites move from

one cell to another cell already visited, they can

be detected again and killed;

• TTL for help request: it represents the maximum

number of hops that a help request can propagate

in the network; it is useful also to avoid loop or

help request diffusion without time limit;

• Time-out help request: it represents the waiting

time of a drone requesting a help by other drones;

after this time a drone can recall the best drones

among all drones answering to help request on the

basis of selection criteria.

In the front-end interface it is possible to select

the data diffusion strategy. In particular, map info can

be disseminated in a restricted ﬂooding with different

maximum-hop. For example a local map distribution

inside the local range of each drone can be considered

a particular case of a restricted ﬂooding with number

of hops equal to 1. If the number of hop increases, the

restricted ﬂooding can become the classical ﬂooding

where all drones are involved in the communication

and can receive the local map of each drone updating

in a fast way the overall network topology.

On the other hand, it is possible in the GUI to se-

lect also a link-state routing as map distribution pro-

tocol in order to build a map and drones topology in

the overall network with the possibility to compute

the minimum cost paths from each drone towards all

drones in the network. The shortest paths are consid-

ering changing the classical minimum-hop count met-

ric with the residual pesticide of drones. This means

that the modiﬁed link-state routing allows to select

drones with higher amount of pesticide guaranteeing

that the infected area can be cured after the arriving

of the involved drones.

Figure 2: Graphical Interface.

4.3 Simulation Results

In the following some simulation results related to

consumed energy, killed parasites and pesticide con-

sumption are shown. It is worth pointing out that the

hand designed simulator is a discrete event simulator.

In Fig. 4 it is shown the consumed energy for

ﬂooding with restriction to 1 hop until 5 hops and

link-state routing. LS and restricted ﬂooding to 3,

4 hops perform better than restricted ﬂooding to 1

hop. Thus is due to the map exchange that is not ex-

ploited when the ﬂooding is too restrictive. On the

other hand, extending the ﬂooding to more hops, the

advantage of the map distribution allow more drones

to reduce their movement in already explored areas.

In Fig. 5 it is shown the number of killed parasites

for restricted ﬂooding and LS. Also in this case it is

perceived a dependence ﬂooding by the number of

hops. For this performance metric the best results are

achieved for restricted ﬂooding with 2-3 hops. LS is a

good compromise between number of killed parasites

and consumed energy. Concerning the last graphic in

Fig. 6, the used pesticide is shown for restricted ﬂood-

ing and LS. In this case, LS is able to perform better

than ﬂooding because the distributed protocol is able

to better consider the residual pesticide among drones

involving in the spraying action drone with more pes-

ticide capacity.

UAVs Team and Its Application in Agriculture: A Simulation Environment

377

Figure 3: GUI to set the simulation parameters.

5 CONCLUSIONS

In this paper a novel simulator working in the agri-

culture domain is proposed. It allows to instantiate

Figure 4: Consumed energy vs topology update time for

ﬂooding and link-state routing with pesticide quantity as

metric.

Figure 5: Killed parasites vs topology update time for ﬂood-

ing and link-state routing with pesticide quantity as metric.

the agriculture ﬁeld, drones with related communica-

tion module and sensor/actuators, parasites with their

local movement and scope and plants with some re-

sistance characteristics. Moreover, two coordination

strategy have been proposed and studied: the ﬁrst

one is based on a local ﬁeld map distribution in or-

der to let neighbor drones about the presence of par-

asites or already visited pieces of land; the second

one is based on the extension of a link-state routing

protocol to the agriculture application domain to dis-

tribute all topology info among all drones. It is shown

as the distributed link-state distribution is more suit-

able since it allows to reduce the energy consumption

and to increase te number of killed parasites. More-

Figure 6: Used pesticide vs topology update time for ﬂood-

ing and link-state routing with pesticide quantity as metric.

SIMULTECH 2017 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

378

over, the distribution of pieces of already visited maps

among all drones allow to speed up the algorithm con-

vergence reducing the number of cells visited more

times. The link-state routing has been modiﬁed to ac-

count more metrics such as remaining pesticide quan-

tity and already visited cells. Future work includes the

introduction of others parameters, coordination tech-

niques, and many constraints related to the domain of

interest.

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