Specification development of robotic system for

pesticide spraying in greenhouse

Vitalijs Komasilovs*,**

, Egils Stalidzans*,**

, Vitalijs Osadcuks*,**

and Martins Mednis*

* Department of Computer Systems, Latvia University of Agriculture, Jelgava, Latvia

** Faculty of Computer Science and Information Technology, Riga Technical University, Riga, Latvia

e-mail: vitalijs.komasilovs@gmail.com, egils.stalidzans@gmail.com, vtl@tvnet.lv, martins@mednis.info

Abstract – Robotic systems are replacing humans in

increasing number of dangerous tasks. Pesticide spraying in

greenhouses is one of tasks where a number of robotic systems

have been developed and tested. Therefore it is the right time

to raise the question about optimisation of the costs efficiency

of robot colony. In case of homogenous robotic system only the

number of universal robots has to be determined. The

drawback is that some subsystems (vision, spraying) might be

utilised at a very low rate and the investment may be

inefficient. To avoid that it is possible to design a

heterogeneous robotic system where the solution space of a

robotic system specification is large and optimisation task

becomes very complex. Still a good solution can reduce costs.

Optimisation of specification of heterogeneous robotic system

for pesticide spraying (plant inspection, spraying and pesticide

transport functions) in a greenhouse with rectangular layout is

performed using genetic algorithms and corresponding open

source GAMBot-Eva software.

Optimisation results demonstrate three groups of solutions

that are within 3% range of lowest possible costs: 1)

homogeneous system of universal robots, 2) heterogeneous

system with two types of robots (inspection-spraying robots

and spraying-transportation robots) and 3) heterogeneous

system with two types of robots without duplicating functions

(inspection-spraying robots and transportation robots).

Applied optimisation approach can be adapted for different

robot missions.

I. INTRODUCTION

Robots are taking over more and more functions from

humans where precision and repeatability in routine tasks

are needed and where human workers are exposed to a

danger. One such task is cultivation of crops in fields and

greenhouses where human operators still manually perform

most operations on the crop although they are often highly

repetitive and sometime even dangerous [1]. Autonomous

mobile robots and their systems are classified as a part of

the Precision Agriculture, which aims to optimize

agricultural field management focusing on the enhancement

of crop knowledge, environmental protection and

economics [2].

Researches on using autonomous mobile robots for

various tasks in greenhouse environment are being

performed for over several decades, but only recent

advances in embedded control systems, optical plant

recognition and localisation methods allowed develop

individual robots and cooperative robot systems (colonies

of robots) ready for in-field tasks. Wherewith topics such as

design of grippers, sprayers, weed control and other plant

harvesting and treatment tools [3], plant recognition [4],

logistics and adaptation of greenhouse environment [1] as

well as frameworks and fully operational prototypes of

individual robots [5] and colonies of robots [6] are widely

discussed.

One of cases where mobile robots can be successfully

used in greenhouses is the pesticide spraying task [7]. Good

progress can be observed in automation of particular

operations like recognition of places on a plant to be

sprayed [8], [9], spraying technologies[10–12], protection

of the robot itself from pesticides [13] and other operations.

Taking into account that in real application more than one

robot will be needed there is still a lack of studies to find

out optimal specification of robotic system to assure that a

functioning robotic system has been developed with

minimal costs trying to utilize efficiently all the

components of system.

The robotic system or colony of robots is defined as a

multi-robot system where some robots are in charge of

some responsibilities to work with others in a cooperative

way to do same tasks, in a collaborative way, to

communicate each other to be more efficient or to take

decisions in a collective way, etc. with a common colony-

wide goal [14]. Colonies of mobile robots could be

effectively used instead of plant cultivation equipment with

fixed position (fixed watering tubes, pesticide sprayers,

fertilizers, conveyors etc.) to allow easily migrate between

types of crops which as a rule results in reconfiguration of

the greenhouse interior.

The specification may be optimised assuming that a

homogenous robotic system is needed and the main task is

to determine the number of necessary robots. In this case all

the robots would be the same and it can happen that some

tools on robots are utilised very seldom thus leaving space

for improvement of return of investments (ROI).

Heterogeneous robotic systems allow more elastic approach

at the costs of increasing space of solutions [15]. Still the

reward of optimisation can be savings of costs which may

well cover the optimisation expenses.

A dedicated procedure for optimisation of the

specification of robot systems has been developed earlier

[16]. That approach is demonstrated using grass mowing

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CINTI 2013 • 14th IEEE International Symposium on Computational Intelligence and Informatics • 19–21 November, 2013 • Budapest, Hungary

978-1-4799-0197-5/13/$31.00 ©2013 IEEE

and collecting task as a case study [17] indicating good

applicability of genetic algorithms [18]. Analysis of results

indicates that different composition strategies of robotic

system can be applied while some rules like proportion of

some implemented units remain constant.

In this research we aim to discover industrial factors in

case of pesticide processing of greenhouse plants that

influence the optimal specification of robotic system

assuming that humans should be eliminated from contact

with pesticides using the approach of Komasilovs [16].

The procedure is applied to the pesticide spraying task in

the greenhouse to find out and classify different

specification approaches with similar return of investments

(ROI) period. It is found that three groups of solutions (one

homogeneous and two heterogeneous) have very similar

ROI period.

II. LAYOUT OF THE GREENHOUSE

During modelling based optimization procedure we have

used rectangular layout of greenhouse (see fig. 1). The

greenhouse was composed of 80 rows with 150 plants on

each row. Interval between rows is 1.5 m, interval between

plants is 1 m. It is assumed that pesticide storage is 50 m

away from greenhouse entrance.

General mission for the robotic system was to inspect the

plants and spray the pesticides where it is needed. Relative

amount of diseased plants was assumed equal to 40%.

It was assumed that overall amount of work for the

system is to perform 1000 full cycles of greenhouse

inspection and spraying mission. One cycle includes one

full treatment of all diseased plants in the particular type of

greenhouse (see fig. 1).

III. FUNCTIONAL DECOMPOSITION OF PESTICIDE

SPRAYING TASK

According to concepts of specification optimization

procedure [15] the mission for the robotic system is defined

using components and tasks.

A. Defining components of robotic system

The components required for pesticide spraying mission

are shown in Table I.

Spending needed for purchasing each component

(investment cost) are expressed in abstract monetary units.

Operating power of each component is defined and is used

for estimating operating cost of the robotic system. We

define additional properties for the components needed for

detailed analysis of dynamic behaviour of the robotic

system.

Mobile robot platform is used to carry all other facilities

needed for the mission. We have considered two alternative

robot platforms: fast platform with moderate weight

carrying capacity (can include small tank of pesticides) and

slower platform with high capacity (can include large

pesticide tank and able to refill small tanks of pesticides).

Distance measurement sensor (range finder) is used for

precise positioning near the plant to enable exact plant

inspection and spraying. We have analysed three sensor

alternatives: laser range finder, distance measurement and

localization using radio beacon and receivers [19], and

distance measurement based on visual information

processing.

High definition camera is used for plant inspection and

disease location. Sprayer is utilized for pesticide infliction

onto the diseased plants and includes internal storage for

pesticides needed for spraying. External pesticide tank is

used for transporting pesticides from the storage to the

greenhouse. The volume of the tank depends on carrying

capacities of robot platform.

Standalone pesticide storage is equipped with automated

docking and refuelling system for approaching robots.

Fig. 1. Greenhouse layout

Table I. Robot components for greenhouse spraying missing

Component Inv.cost,

m.u.

Power,

W Properties

Light platform 4000 500 Max weight: 10 kg Max speed: 3 m/s

Length: 0.2 m

Heavy platform 7000 900 Max weight: 60 kg

Max speed: 1 m/s

Length: 0.4 m

Laser range

finder

800 2 Tolerance: 0.05 m

Sample rate: 100 Hz

Radio receiver

for beacon based

localization

10 2 Tolerance: 0.5 m

Sample rate: 100 Hz

Visual range finder

50 2 Tolerance: 0.2 m Sample rate: 3 Hz

Radio beacon 1000 100 -

HD Camera 70 2 -

Sprayer 50 5 -

Tank 100 5 -

Pesticide storage 500 25 -

V. Komasilovs et al. • Specification Development of Robotic System for Pesticide Spraying in Greenhouse

454

B. Defining mission tasks

We distinguish three tasks in global greenhouse spraying

mission: plant inspection task, when the diseased plants are

located; spraying task when the diseased plants are being

sprayed with pesticides; and transportation task, when

pesticides are transported from the storage to the worksite.

Specification optimization procedure requires two

features to be defined for each task: total amount of work

for the robotic system, and function used for calculating

performance of a particular agent. In order to express agent

performance we calculate the time needed to accomplish

single unit of work of the task.

For the inspection task total amount of work is a

number of plants within greenhouse (1).

(1)

where

– inspection task work amount (plant);

– number of rows in greenhouse;

– number of plants in row.

We distinguish three phases of inspection task and

calculate time required to perform it for particular agent.

First, robot is moving between plants (2), positions near the

plant (3) and then inspects the plant.

(

) ⁄ (2)

where

– time for moving between plants (s);

– distance between plants (m);

– platform approaching distance (m);

– platform moving speed (m/s).

⁄

(

)⁄ (3)

where

– positioning time (s);

– robot positioning speed (m/s);

– positioning tolerance for task (m);

– position sensor sampling rate (1/s).

Agent performance for inspection task is calculated from

a time needed for inspecting single plant (4).

(

)⁄ (4)

where

– agent performance for the task (1/s);

– inspection time (s).

Total amount of spraying task is a number of diseased

plants in the greenhouse (5).

(5)

where

– spraying task work amount (plant);

– relative amount of diseased plants.

We distinguish three phases of spraying task similarly to

inspection task. Robot moves between diseased plants (6),

positions near plant (7) and sprays pesticides.

[(

)

⁄ ] ( )⁄ (6)

where

– time for moving between plants (s);

– max. moving speed for the task (m/s).

⁄

(

)⁄ (7)

where

– positioning time (s);

– positioning tolerance for task (m).

Agent performance for spraying task is calculated from a

time needed for spraying single plant (8).

(

)⁄ (8)

where

– agent performance for the task (1/s);

– spraying time (s).

For the transportation task total amount of work is

expressed in transported litres of pesticides (9).

(9)

where

– transporting task work amount (l);

– pesticide volume per plant (l).

We define three phases of transportation task as follows:

robot is refuelling at pesticide storage (10), moves to the

greenhouse (11) and then moves to working site within the

greenhouse (12).

⁄ (10)

where

– pesticide refueling time (s);

– transport volume (l);

– pesticide flow speed (l/s).

(

)⁄ (11)

where

– time for moving to greenhouse (s);

– distance to storage (m);

– max. moving speed for the task (m/s).

( ) ⁄ (12)

where

– time for moving to worksite (s);

– distance between rows (m);

Agent performance for transportation task depends on a

transport volume and a time needed for transporting single

litre of pesticide (13).

(

)⁄ (13)

where

– agent performance for the task (1/s).

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CINTI 2013 • 14th IEEE International Symposium on Computational Intelligence and Informatics • 19–21 November, 2013 • Budapest, Hungary

Used task parameters and their values are summarized in

Table II.

IV. SET-UP OF GENETIC ALGORITHM FOR SPECIFICATION

OPTIMISATION

GAMBot-Eva software [20] was used for specification

optimization of robotic system which is based on genetic

algorithm and allows fast evaluation of large number of

solution candidates.

Genetic representation of the solution candidate is based

on a list of agents and a number of their instances used

within the solution [18].

A list of feasible agents is obtained by combining

recently defined components and applying constraints. The

list of constraints is determined by the greenhouse spraying

mission and is used to eliminate illogical component

combinations. The list includes following constraint

entities:

single type of robot platform is allowed for an agent;

single type of position sensor is allowed for an

agent;

position sensor, camera, sprayer and tank should be

mobile (placed on mobile platform);

pesticide storage should be stationary;

agent has to be able to perform at least one task

( ).

The total number of possible combinations of

components exceeds . Taking into account

defined constraints the feasible solution space is reduced

down to combinations, which are considered

during optimization.

GAMBot-Eva specific parameters used during

experiments are as follows:

maximum number of agent instances: 30;

GA generations limit: 2000;

processing batch size: 50;

population size: 100;

mutation rate: of genes;

crossover rate: 35% of chromosomes;

randomization rate: 5% of chromosomes.

V. OPTIMIZATION RESULTS AND DISCUSSION

During experiments we have repeated specification

optimization procedure for 20 times using aforementioned

setup of greenhouse spraying mission. Because of

stochastic nature of optimization approach used within the

procedure obtained results might change between runs.

The output of the optimization procedure is a formal

specification of robotic system, which lists required agents

(combinations of the components) and the number of their

instances, e.g. 3x ([ranger-visual][platform-light][sprayer]).

According to results of 20 optimisation runs three

solutions have costs within 3% of the best value of

objective function found.

Most of the proposed solutions (55%) specify the

homogeneous robot system containing identical robots

which are able to perform all three tasks of the mission:

inspect plants, spray pesticides and transport them from

storage to worksite. The solution implies application of 5

identical robots based on light platform and equipped with

camera, sprayer, pesticide tank and visual range finder.

Estimated total cost of ownership (TCO) for such system is

172 358 monetary units. Another large group of proposed solutions (30%) stand

for semi-heterogeneous system with two types of robots.

The solution implies application of 2 robots which are able

to perform inspection and spraying tasks (build onto light

platform and equipped with camera, sprayer and laser range

finder) and 3 robots capable to perform spraying and

transportation task (build onto light platform and equipped

with sprayer, pesticide tank and visual range finder).

Estimated TCO for this type of system is 174 560 monetary

units.

Only 10% of proposed solutions defined robot system

specifications with no duplicating functions. Example

solution is composed of 5 agents capable to perform

inspection and spraying task (light platform, visual range

finder) and 1 agent dedicated for transportation task (heavy

platform, radio beacon and receiver based range finder).

Another example solution is composed of 1 agent capable

to perform inspection and transportation task (heavy

platform, laser range finder) and 4 agents dedicated for

spraying task (light platform, visual range finder).

Estimated TCO of these solutions is 177 244 monetary

units.

The rest of proposed solutions (5%) stand for custom

composition of agents and can be considered as experiment

exception.

Among robot platform alternatives preference is given to

the light platform (98% of agents). It could be explained by

faster moving time and lower power consumption. Heavy

platforms have lower benefit even taking into account much

higher workload capacity.

Among sensor alternatives preference is given to visual

distance measurement (71% of agents) and to laser range

finders (26% of agents). It is explained by sufficient

Table II. Task parameters for greenhouse spraying mission

Parameter Symbol Value

Number of rows in greenhouse 80

Number of plants per row 150

Distance between plants (m) 1.0

Distance between rows (m) 1.5

Positioning tolerance for inspection task (m) 0.5

Inspection time (s) 1.0

Relative amount of diseased plants 0.4

Max. moving speed for spraying task (m/s) 1.5

Positioning tolerance for spraying task (m) 0.2

Spraying time (s) 10.0

Pesticide volume per plant (l) 0.2

Pesticide flow speed at storage (l/s) 0.1

Distance between greenhouse and storage (m) 50.0

Max. moving speed for transporting task (m/s) 2.0

V. Komasilovs et al. • Specification Development of Robotic System for Pesticide Spraying in Greenhouse

456

tolerance of visual measurement system for all tasks and by

lower costs. The potential of laser range finders with high

tolerance stays unused during the tasks. While radio beacon

and receiver based distance measurement is suitable only

for inspection task and has relatively high costs.

CONCLUSIONS

Specification optimisation procedure for heterogeneous

robotic systems can be applied for pesticide spraying task in

a greenhouse to determine the groups of best solutions to

support the decision about the solution of robotic system to

be implemented. In case of necessity more exact evaluation

can be done using dynamic simulations on playgrounds like

Player/Stage or similar software. The main advantage of

proposed method is automatic screening of very large

solution space avoiding the risk to implement greenhouse

specialist’s intuition-based solution which might be far

from optimum in terms of costs.

According to the performed optimisations only three

types of solutions out of large solution space were

competitive taking into account the costs of purchase and

exploitation for given number of operation cycles.

It turned out that homogeneous robot system is

competitive compared with two best heterogeneous

systems: 1) heterogeneous system with two types of robots

(inspection-spraying robots and spraying-transportation

robots) and 2) heterogeneous system with two types of

robots without duplicating functions (inspection-spraying

robots and transportation robots). The total costs of best

solutions among all the three groups lay within 3%. Refined

search including additional criteria can be performed in this

case.

In case if the competitors of best solution are much worse

according to objective function (in this case it was costs)

more detailed comparison may be skipped.

ACKNOWLEDGMENTS

The research was supported by the European Regional

Development Fund within the project „Development of

technology for multiagent robotic intelligent system”.

Agreement No. 2010/0258/2DP/2.1.1.1.0/10/APIA/VIAA/

/005.

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