System to Predict Diseases in Vineyards and Olive Groves using Data

Mining and Geolocation

Luís Alves

1

, Rodrigo Rocha Silva

2,3

and Jorge Bernardino

1,3

1

DEIS, Polytechnic Institute of Coimbra - ISEC, Coimbra, Portugal

2

FATEC Mogi das Cruzes, São Paulo Technological College, Mogi das Cruzes, São Paulo, Brazil

3

CISUC, Centre for Informatics and Systems of the University of Coimbra, Coimbra, Portugal

Keywords: Data Mining, Geolocation, Agriculture, Crop Diseases.

Abstract: In recent years, producers have complained about the disease attacks in their crops, due in large part to the

weather conditions that lead to heavy losses. Information and communication technology in agriculture

offers a wide range of solutions to some agricultural challenges. This technology that allows progress of the

agricultural sector can increase the productivity and profitability of a farm. This paper intends to propose a

System to predict diseases in Vineyards and Olive Groves using data mining and geolocation. Grapevine

Downy Mildew, Powdery Mildew, Peacock Spot and Olive Anthracnose are the diseases used to test system

because they are diseases that cause large losses in production that result in very small profits and large

economic losses. The system captures and stores climatic, environmental data as well as data of the

producers and their properties. The data collected by the system is used to predict diseases using data

mining. We choose Random Forest algorithm provided by Weka, an open source system that provides a

collection of visualization tools and algorithms for data analysis and predictive modelling, to calculate the

probability of diseases occurrence. The main objective of the system is to help producers in a preventive

way so that there is less loss in the production of such agricultural crops.

1 INTRODUCTION

Nowadays, Information Technology (IT) is very

important for the development of various sectors

(Kapoor, 2014). In any sector, information is the key

for its development and, the agricultural sector is not

an exception to it (Allahyari and Chizari, 2010).

Agriculture is one of the most important sectors

in the world being a major livelihood for a lot of

people (Goswami, Matin and Aruma, 2012), and

could benefit tremendously from the applications of

ICTs. If relevant and right information is provided in

the right time, it can help agriculture a lot. It helps

take timely action, prepare strategies for next season

or year, speculate the market changes and avoid

unfavorable circumstances. The potential of IT can

be assessed as a tool for direct contribution to

agricultural productivity and as an indirect tool for

empowering farmers to take informed and quality

decisions which will have positive impact on the

way agriculture and allied activities are conducted

(Kapoor, 2014). IT has made its way into the

agricultural sector, and with positive results such as

improved decision making, better planning,

community involvement, agricultural breakthroughs

and agriculture for everyone (Mitra, 2014). So, the

development of agriculture may depend on how fast

and relevant information is provided to the end

users.

In recent years, producers have complained

about the disease attacks in their crops, due - in large

part - to the weather conditions that lead to heavy

losses (Ghini, Hamada and Bettiol, 2008). Grapevine

Downy Mildew (Plasmopara Viticola) and Powdery

Mildew (Uncinula necator) are two of the most

important diseases that infect Vineyards and

Peacock Spot (Cycloconium oleaginum) and Olive

Anthracnose (Gloeosporium Olivarum) diseases are

two of the most important diseases that infect Olive

Groves worldwide. These diseases cause large losses

in production that result in very small profits and

large economic losses. Given the economic

importance of these diseases, their occurrence can be

prevented and reduced through the correct use of

digital information.

Alves, L., Silva, R. and Bernardino, J.

System to Predict Diseases in Vineyards and Olive Groves using Data Mining and Geolocation.

DOI: 10.5220/0006914306790687

In Proceedings of the 13th International Conference on Software Technologies (ICSOFT 2018), pages 679-687

ISBN: 978-989-758-320-9

679

We propose a System to predict diseases in

Vineyards and Olive Groves using data mining and

geolocation. We test Random Forest classification

data mining algorithm in agricultural domain to

predict some transmissible diseases in vines and

olive groves based on symptoms and weather data

Data mining is an emerging technology that can

aid in the discovery of rules and patterns in sets of

data. The potential applications of data mining

techniques in domains such as agriculture are

numerous (Milovic and Radojevic, 2015). Applying

data mining in the agricultural field make possible

extract useful knowledge and trends (Milovic and

Radojevic, 2015). Knowledge gained with the use of

data mining techniques can be used to increase work

efficiency and improve decision making quality that

will rise success of the agricultural sector (Milovic

and Radojevic, 2015). Appropriate technology and

analytical techniques are required for measuring data

mining results, as well as reporting and tracking

systems. Agricultural organizations that use data

mining applications have the possibility to predict

future risk of disease and to make adequate

decisions about their treatments. We choose one of

the most popular software tools, called Weka, that

gathers a number of schemes and allows users to run

them on real – world data sets and interpret and

compare the results.

The main contributions of this paper are a new

approach for using data mining and geolocation in

agricultural domain to predict some transmissible

diseases in vines and olive groves based on

symptoms and weather data that will assist the

producer and help reduce unnecessary costs.

The remainder of this paper is organized as

follows. Section 2 describes related work. Section 3

describes the proposed System. Section 4 describes

geolocation. Section 5 describes weather forecasting.

Section 6 describes experimental evaluation.

Section 7 discuss the results. Finally, Section 8

concludes the paper and presents future work.

2 RELATED WORK

Information technology is more and more

implemented in agriculture enterprises in order to

respond to the needs of agronomists and managers in

their daily decision making activities (Milovic and

Radojevic, 2015).

Mobile apps in the agriculture sector can be the

best option to increase agricultural countries

production (Patel and Patel, 2016).

In this section we talk about some existing

application in agriculture field.

FarmManager – The management of small

farms, designed and developed to respond to the

needs and characteristics of farmers. It can store

database, do farm customization, easy field

management, land field data, easy job recording

process, employees and equipment (Lantzos,

Koykoyris and Salampasis, 2013).

AgroMobile – Developed specially to assist

farmers in agricultural needs. It is used for botanical

species recognition and disease detection using a

simple mobile phone with camera (Prasad, Peddoju

and Ghosh, 2013).

Agriculture Supply Chain Management – The

complete package for farmers who want to do

farming on sugarcane and obtain good production

with proper management (Chirmade et al., 2015).

E-agree – Used to detect leaf diseases. Also

provides online market place, weather report, market

rate guide and soil information to the farmer (Reddy

et al., 2015).

Agricultural Decision Support System –

Provide information for cultivation of various kinds

of crops in various type of atmosphere (Koli and

Jadhav, 2013).

Krishi Ville – It takes care of the updates of the

different agricultural commodities, weather forecast

updates or agricultural news updates. The

application has been designed taking Indian farming

into consideration (Singhal, Verma and Shukla,

2011).

Scheduling, Controlling and Monitoring of

Agricultural Devices – Used by the farmer for

remotely controlling the motor and pesticides

proportion, monitoring the farming activities going

on in the farm. It also allows improving the

efficiency of the irrigation process (Choudhary et

al., 2015).

MahaFarm – Helps in their farming activities

that include Agro based crop information, weather

updates, daily market prices and news informational

updates (Bhave, Joshi and Fernandes, 2014).

Solution for farmer consumer interaction –

Provides information to farmers regarding how to

get access to better inputs and gain more

productivity. It also gives information such as the

activities he should perform right from the time

when the seed germinates till the day when the crop

is ready to be harvested (Radhika, 2015).

Kharif and Rabi Crop Diseases Information –

Provides all Kharif and Rabi diseases details

information such as types of diseases, pesticides

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680

used, method for applying pesticides (Patel, Thakkar

and Desai, 2014).

Irrigation Control System – Efficient use of

water and power. Helps the farmer to ON/OFF the

motor without his physical presence in the field

(Shabadi et al., 2014). Irrigation Control system is

only directed to control the efficient use of water and

power and not for crop information, weather forecast

and farm management.

Farmer Helping Services – Offers all the

Horticulture information about flowers, fruits and

vegetables (Patel, Thakkar and Radadiya, 2014).

Farmer Helping Services has advantages like

horticulture information but does not allow

producers to manage their farms and neither informs

about risk of diseases to occur.

Agrobase – “Agrobase” is an app designed to be

easy to use in the field, practical assistance for

agricultural consultants, farmers, trainees and

students of Agronomy and other agricultural related

areas. This app helps agronomists, farmers,

distributors and agricultural contractors easily find a

product by searching for active material, name,

category or culture. In recognitions in the field,

farmers and agronomists can easily identify weeds,

pests, insects, diseases, looking for common name,

Latin name, category or culture (Agrobase, 2017).

OpenPD – OpenPD is a mobile app that

provides an on-the-field and on-the-fly system for

fast identification of plant pests and diseases. It is

easy to use, based on open community and peer

learning (OpenPD, 2017).

We’ve found that many of the apps are static.

Instead of that, dynamic apps will be better to use.

The main difference between these apps and ours

work is that we make a system which use data

mining to predict diseases occurring on the basis of

climatic, environmental and other favorable

variables and geolocation to help farmers in their

farms. This System has a list of functionalities into

the one single app.

3 THE SYSTEM

Finding digital content related to agriculture is easy

but requires searching at various sources and

sometimes, the data is ambiguous and incomplete.

The data regarding farming are available from many

sources like printed media, audio and visual aids,

newspaper, TV, internet, mobile and others but the

formats and structures of data are dissimilar. It’s

very hard for a farmer to get the information and

understand the various pieces of information which

are disseminated from various sources. Sometimes

many manual steps are required while processing

data for transforming data from one format to

another (Patel and Patel, 2016).

Many times, a farmer gets confused when taking

decision regarding the selection of fertilizer,

pesticide and the time to take particular farming

actions.

3.1 The Propose

Mobile and Web apps in the arena of agriculture can

be the best option to increase countries agriculture

production. The goal is to create a system where

every user related with agriculture can manage his

crops, protecting them from diseases.

The main objective of this system is to help the

farmer in his farms and make all the useful

information available in a system. This system

provides various features such as:

 User registration;

 Property registration;

 Symptoms registration in a given culture;

 Disease risk prediction based on symptoms and

weather data;

 Geolocation of all properties;

 Risk history of disease in properties;

 Registry of disease occurrence;

 Query of weather data hourly and daily;

 Property monitoring;

 Regions registration.

Our aim is to take advantage of meteorological

data collected by devices in properties and data

coming from external APIs, symptoms registration

in a given agriculture by farmers and other data for

predicting risk of diseases. Merging these two

concepts (data collected and diseases) can bring

some advantages, such as:

 Cost Reductions;

 Assisted task execution;

 Greater competitiveness in the agricultural

sector;

 Productivity increase.

Data mining is used for predicting risk of

diseases, bringing more improvements in the

recommendation and searching of treatments.

We’ve chosen Weka - an open source system that

provides collection of visualization tools and

algorithms for data analysis and predictive

modelling (Milovic and Radojevic, 2015). We have

conducted a comparison study based on accuracy

between algorithms provided by Weka,

System to Predict Diseases in Vineyards and Olive Groves using Data Mining and Geolocation

681

corresponding to different classification and the

Random Forest algorithm was the best (Figure 1).

Figure 1: Accuracy measure of different algorithms.

So, the data mining classification technique Random

Forest is used in order to calculate risk probability of

disease. We calculate the probability of diseases’

risk based on symptoms and weather data. In the

System, Weka allows us to display diseases risk

probability like it is shown in Figure 2. Figure 2

displays a chart showing the probability of two

diseases occurring in a property for the next 7 days.

The x axis represents the date and y axis represent

the probability value.

Figure 2: Disease Risk Prediction System.

In order to integrate the two applications (Web

and Mobile) we created a functionality that allows

association of symptoms with the stages of

cultivation.

For associating the Web application symptoms

with the Mobile application, we use an API that

communicate with Web application database and

returns a JSON object with the diseases, their

symptoms and the date those symptoms have been

inserted.

3.2 The Architecture

Figure 3 shows the architecture of our system and

how its components communicate between

themselves. Next, we will describe every component

and its main function.

Figure 3: System architecture.

Client applications are the applications that allow

the user to interact with the system and manage its

properties. These applications will communicate

with the server through the developed API, which is

based on HTTPS (Hyper Transfer Protocol Secure)

protocol. The data sent is in JSON (JavaScript

Object Notation) format and consists of properties

data.

Application Server is the first responsible for

supporting client applications, containing a RESTful

API to handle the requests of applications.

In order to save data, this architecture provides a

database. The Mysql database will be the database

that stores all data used by the applications described

above. This database will be updated periodically to

improve diseases prediction.

Finally, the external APIs are the APIs

responsible to provide meteorological data,

geolocation and data mining tasks.

4 GEOLOCATION

In this system, the use of geolocation allows location

of all land fields and farming tasks on Google Maps

API (Google Maps API, 2017). Information

collected from the use of geolocation can be

integrated to create field management strategies for

chemical application, cultivation and harvest.

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682

4.1 Google Maps API

In the System, Google Maps API allows to register

properties and regions on the map like it is shown in

Figure 4.

Figure 4: System Region and Properties.

Properties are identified by a red marker and regions

are identified by a green area. A region can have

multiple associated properties and a property can be

associated with multiple regions. The properties will

have equipment that collects data from the

environment installed. Regions are important in the

proposed system to prevent property belonging to a

region in which a particular disease has occurred.

Having equipment associated with a region, enables

us to control all properties in that region.

5 WEATHER FORECASTING

Agricultural activities are very sensitive to climate

and weather conditions (Ghini, Hamada and Bettiol,

2008). Agricultural decision-maker can either be at

the mercy of these natural factors or try to benefit

from them. Climatological data is essential in

planning agricultural production.

For agriculture diseases, the minimum weather

data set required should consist of temperature and

humidity or derived parameters such as accumulated

heat or degree-days.

Real time meteorological information can also be

effectively used in agricultural production process.

Many times, the risk of diseases in cultures of

agriculture is related to favorable weather

conditions. So, weather data can increase

agricultural yield.

The quality and quantity of agricultural

production can be increased, and production costs

decreased. With weather data is also possible to

recognize bad weather conditions and be more

prepared to minimize the damage.

This System provides weather updates and in

case of diseases risk it alerts the farmers via e-mail,

text messages and mobile notifications. It provides

the weather conditions for the property location and

calculates probability of disease risk based on

collected data to assist the producer in undertaking

actions for healing and prevention at the correct

time, avoiding unnecessary spraying.

6 EXPERIMENTS

In the experimental evaluation, in order to test

system, we choose four diseases to predict

probability of risk. We choose Grapevine Downy

Mildew (Plasmopara Viticola), Powdery Mildew

(Uncinula necator), Peacock Spot (Cycloconium

oleaginum) and Olive Anthracnose (Gloeosporium

Olivarum) diseases to make the experimental

evaluation because we consider that they are the four

most important diseases that infect Vineyards and

Olive Groves worldwide based on farmers’ opinion.

In Weka, the datasets are given in ARFF

(Attribute Relation File format) format which is

compatible with this software. In order to perform

these experiments, we created four datasets. Each of

them has attributes that correspond to the most

important symptoms and weather data favorable to

the development of various diseases. DataSets -

Plasmopara Viticola, Uncinula necator,

Cycloconium oleaginum and Gloeosporium

Olivarum:

 DataSet 1: This dataset is a generated dataset

with 4200 instances and correspond to

Plasmopara Viticola disease. This dataset

contains 1900 instances with probability of

disease and 2300 without it.

 DataSet 2: This dataset is a generated dataset

with 141757 instances and correspond to

Uncinula necator disease. This dataset contains

35036 instances with probability of disease and

106721 without.

 DataSet 3: This dataset is a generated dataset

with 1760 instances and correspond to

Cycloconium oleaginum disease. This dataset

System to Predict Diseases in Vineyards and Olive Groves using Data Mining and Geolocation

683

contains 185 instances with probability of

disease and 1575 without.

 DataSet 4: This dataset is a generated dataset

with 2800 instances and correspond to

Gloeosporium Olivarum. This dataset contains

330 instances with probability of disease and

2470 without it.

Each of these datasets has attributes that correspond

to the most important causes and symptoms to

development of diseases.

DataSet 1 has the following attributes:

 tmp: This matches the temperature. The

temperature is important for the development of

this disease when it has values higher than 11ºC.

 hmdt: This matches the humidity. The humidity

is important for the development of this disease

when it has values higher than 92%.

 rn: This matches the precipitation. The

precipitation is important for the development of

this disease because the fungus requires free

water in the tissues for a minimum of 2 hours for

infection.

 TPS: This matches top page of the leaf with

spot. This attribute is one of the main symptoms

of this disease.

 CP: This matches curving peduncle symptom.

 WSS: This matches white spots on the lower

page of the sheet symptom.

 SB: This matches stains on the branches

symptom.

 diss: This matches the possibility of the disease

occurring based on the previous attributes.

The DataSet 2 has the following attributes:

 tmp: This matches the temperature. The

temperature is important for the development of

this disease when it has values higher than 15ºC.

 hmdt: This matches the humidity. The humidity

is important for the development of this disease

when it has values higher than 25%.

 rn: This matches the precipitation. The

precipitation is important for the development of

this disease because the fungus requires free

water.

 STS: This matches Stains on the Sheet symptom.

 ESBL: This matches Edges of slightly beaded

leaves symptom.

 NL: This matches Necrosis on leaves symptom.

 BD: This matches Berry with dust symptom.

 CRB: This matches Cracked berries symptom.

 CTB: This matches Coated branches symptom.

 SB: This matches Stained branches symptom.

 ID: This matches Inflorescences with dust

symptom.

 diss: This matches the possibility of the disease

occurring based on previous attributes.

The DataSet 3 has the following attributes:

 tmp: This matches the temperature. The

temperature is important for the development of

this disease when it has values between 15ºC and

20ºC.

 hmdt: This matches the humidity. The humidity

is important for the development of this disease

when it has values higher than 98%.

 rn: This matches the precipitation. The

precipitation is responsible for the spread of the

disease.

 SSP: This matches Stains on the superior page

sheet symptom.

 SIP: This matches Stains on the inferior page

sheet symptom.

 SF: This matches Stains on the fruits symptom.

 diss: This matches the possibility of the disease

occurring based on the previous attributes.

The DataSet 4 has the following attributes:

 tmp: This matches the temperature. The

temperature is important for the development of

this disease when it has values between 20ºC and

25ºC.

 hmdt: This matches the humidity. The humidity

is important for the development of this disease

when it has values higher than 92%.

 rn: This matches the precipitation. The

precipitation is responsible for the spread of the

disease.

 RSF: This matches rounded spots on fruits. This

is one of the main symptoms of this disease.

 WF: This matches wrinkled fruits symptom.

 diss: This matches possibility of the disease

occurring based on the previous attributes.

We set random values for all attributes of

datasets and calculate probability of diseases

occurring.

6.1 Grapevine Downy Mildew

For the Grapevine Downy Mildew data set we set

some attributes like:

 Temperature - C1 (ºC);

 Humidity - C2 (%);

 Precipitation Occurrence - C3 (Yes/No);

 Yellowish leaves - S1 (Yes/No);

 Curving peduncle - S2 (Yes/No);

 White spots on the lower page - S3

(Yes/No);

 Stains on the branches - S4 (Yes/No).

ICSOFT 2018 - 13th International Conference on Software Technologies

684

The results of application Random Forest

algorithm to calculate risk probability of Grapevine

Downy Mildew disease are shown in Table 1.

For example, in Test 10 the probability of

disease risk is 87% and values of attributes are:

 Temperature (C1) - 11ºC;

 Humidity (C2) - 63%;

 Precipitation Occurrence (C3) - Yes;

 Yellowish leaves (S1) - Yes;

 Curving peduncle (S2) - No;

 White spots on the lower page (S3) - No;

Stains on the branches (S4) – No.

When applying Random Forest algorithm to

Grapevine Downy Mildew dataset, the results were

satisfactory but could be improved if real property

data had been collected.

Table 1: Tests to Grapevine Downy Mildew disease.

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

C1

26

4

35

39

14

20

5

32

3

11

C2

78

36

67

41

71

88

50

41

97

63

C3

N

Y

N

Y

N

Y

S1

N

Y

N

Y

N

Y

N

Y

S2

Y

N

Y

N

S3

N

Y

N

S4

Y

N

Y

N

P

8

0

4

3

10

7

3

0

2

87

6.2 Powdery Mildew

For the Powdery Mildew data set we set some

attributes like:

 Temperature - C1 (ºC);

 Humidity - C2 (%);

 Precipitation Occurrence - C3 (Yes/No);

 Stains on the Sheet - S1 (Yes/No);

 Edges of slightly beaded leaves - S2 (Yes/No);

 Necrosis on leaves - S3 (Yes/No);

 Berry with dust - S4 (Yes/No);

 Cracked berries - S5 (Yes/No);

 Coated branches - S6 (Yes/No);

 Stained branches - S7 (Yes/No);

 Inflorescences with dust - S8 (Yes/No);

When applying Random Forest algorithm to

Powdery Mildew dataset, we set random values for

the eleven attributes of this dataset in ten instances.

The results of application Random Forest

algorithm to calculate risk probability of Powdery

Mildew disease are shown in Table 2.

For example, in Test 5 the probability of disease

risk is 98% and values of attributes are:

 Temperature (C1) - 29ºC;

 Humidity (C2) - 67%;

 Precipitation Occurrence (C3) - Yes;

 Stains on the Sheet (S1) - Yes

 Edges of slightly beaded leaves (S2) - Yes;

 Necrosis on leaves (S3) - No;

 Berry with dust (S4) - No;

 Cracked berries (S5) - Yes;

 Coated branches (S6) - No;

 Stained branches (S7) - No;

 Inflorescences with dust (S8) - No;

When applying Random _Forest algorithm to

Powdery Mildew dataset, the results are satisfactory

but could be improved if real property data had been

collected.

Table 2: Tests to Powdery Mildew disease.

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

C1

23

7

21

20

29

8

35

12

20

35

C2

41

74

97

27

67

59

91

29

26

74

C3

N

Y

N

Y

N

S1

N

Y

N

Y

N

Y

N

S2

Y

N

Y

N

S3

N

Y

N

Y

N

S4

Y

N

Y

N

Y

N

S5

N

Y

N

Y

N

Y

N

Y

S6

N

Y

N

Y

S7

N

Y

N

Y

N

Y

N

S8

N

Y

N

Y

N

Y

N

P

99

98

97

5

98

1

3

0

2

0

6.3 Peacock Spot

For the Peacock Spot data set we set some attributes

like:

 Temperature - C1 (ºC);

 Humidity - C2 (%);

 Precipitation Occurrence - C3 (Yes/No);

 Stains on the superior page sheet - S1 (Yes/No);

 Stains on the inferior page sheet - S2 (Yes/No);

 Stains on the fruits - S3 (Yes/No);

Table 3: Tests to Peacock Spot disease.

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

C1

16

25

28

26

11

17

15

25

27

16

C2

83

90

76

91

74

97

89

88

85

C3

Y

N

Y

N

Y

S1

N

Y

N

Y

S2

Y

N

Y

N

Y

S3

Y

N

Y

N

P

82

17

22

30

25

85

97

20

70

64

When applying Random Forest algorithm to

Peacock Spot dataset, we set random values for the

six attributes of this dataset in ten instances.

The results of application Random Forest

algorithm to calculate risk probability of Peacock

Spot disease are shown in Table 3. For example, in

System to Predict Diseases in Vineyards and Olive Groves using Data Mining and Geolocation

685

Test 9 the probability of disease risk is 70% and

values of attributes are:

 Temperature (C1) - 27ºC;

 Humidity (C2) - 88%;

 Precipitation Occurrence (C3) - Yes;

 Stains on the superior page sheet (S1) - Yes;

 Stains on the inferior page sheet (S2) - Yes;

 Stains on the fruits (S3) - Yes;

When applying Random Forest algorithm to

Peacock Spot dataset, the results were satisfactory

but could be improved if real property data had been

collected.

6.4 Olive Anthracnose

For the Olive Anthracnose data set we set some

attributes like:

 Temperature - C1 (ºC);

 Humidity - C2 (%);

 Precipitation Occurrence - C3 (Yes/No);

 Rounded spots on fruits - S1 (Yes/No);

 Wrinkled fruits - S2 (Yes/No);

Table 4: Tests to Olive Anthracnose disease.

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

C1

16

24

29

18

27

15

30

25

26

23

C2

96

98

95

92

97

98

92

99

92

C3

Y

N

Y

N

Y

N

S1

Y

N

Y

N

Y

N

Y

S2

Y

N

Y

N

Y

P

30

89

10

15

97

15

5

30

20

97

When applying Random Forest algorithm to

Olive Anthracnose dataset, we set random values for

the five attributes of this dataset in ten instances.

The results of application Random Forest

algorithm to calculate risk probability of Olive

Anthracnose disease are shown in Table 4.

For example, in Test 2 the probability of

disease risk is 80% and values of attributes are:

 Temperature (C1) - 24ºC;

 Humidity (C2) - 98%;

 Precipitation Occurrence (C3) - No;

 Rounded spots on fruits (S1) - No;

 Wrinkled fruits (S2) - No;

When applying Random Forest algorithm to

Olive Anthracnose, the results were satisfactory but

could be improved if real property data had been

collected.

7 DISCUSSIONS OF RESULTS

We use Grapevine Downy Mildew (Plasmopara

Viticola), Downy Mildew (Uncinula Necator),

Peacock Spot (Cycloconium Oleaginum) and Olive

Anthracnose (Gloeosporium Olivarum) Data Sets.

Grapevine Downy Mildew dataset has 4200

samples with 7 independent variables and one class

variable. Powdery Mildew dataset has 141757

samples with 11 independent variables and one class

variable. Peacock Spot dataset has 1760 samples

with 6 independent variables and one class variable.

Olive Anthracnose dataset has 2800 samples with 5

independent variables and one class variable.

The data mining tool Weka is used for

experiment because it is an open source system that

provides a collection of visualization tools and

algorithms for data analysis and predictive

modelling.

The results are better than expected. However,

we need to collect real time data from Vineyards and

Olive Groves to make these results more reliable.

8 CONCLUSIONS AND FUTURE

WORK

Predictive data mining is becoming an essential

instrument in agriculture. Understanding the main

issues underlying these methods and the application

of agreed and standardized procedures are

mandatory for their deployment and the

dissemination of results. Agriculture organizations

that use data mining applications have the possibility

to predict future risk of diseases and make adequate

decisions about their treatments.

Nowadays, the use of smart phone technology

with specialized software shall boost the

management of farms to a high level. This proposed

System helps in all manners, that is, in weather

forecasting, crop analysis and understanding it more

clearly. The use of this System can overleap the high

difficulties of a farm. Using data mining and

geolocation in the agriculture sector can show us an

overview of current practices and challenges in this

sector. We believe that this System is a useful tool

for farm management.

As a future work, we can improve the system

architecture by introducing many more diseases for

risk predicting and more platforms can also use this

application. Real data from farms need to be

collected to improve prediction of disease risk.

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686

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