Paddy Diseases Identification with Texture Analysis using Fractal Descriptors Based on Fourier Spectrum

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2013 International Conference on Computer, Control,

Informatics and Its Applications (IC3INA)

19-21 November 2013

Jakarta,

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IEEE Catalog Number

ISBN

Rifki Sadikin, Agus Subekti

IEEE

CFP13YAD-ART

978-1-4 799-1078-6

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PROCEEDING

2013 International Conference on

Computer, Control, Informatics and

Its Applications (IC3INA)

"Recent Challenges in Computer, Control and

Informatics "

ＱｾＭＲＱ＠

November 2013

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Jakarta, Indonesia, 19-21 November 2013

Committee and Reviewers

Scientific Committee

Andria Arisa!

Adrian

B.

Suksmono

Ana Hadiana

Anitawati Mohd Lokman

Anto S. Nugroho

Agus Subekti

Bernadetta K wintiana Ane

Chan Basaruddin

Didi Rosiyadi

Esa Prakasa

Estiko Rijanto

Hilman F. Pardede

John E. Batubara

Kuncoro Wastuwibowo

Laksana T. Handoko

Moncef Gabbouj

Mahmud M. Hasan

Mohd Y. Mashor

Rifki Sadikin

Roberto Togneri

Research Center for Informatics LIPI, Indonesia

School of

ｾｬ･｣ｴｲｩ｣｡ｬ＠

Engineering and Informatics, Bandung

Institute of Technology, Indonesia

Research Center for Informatics LIPI, Indonesia - Chair

U niversiti Teknologi MARA, Malaysia

Center for Information and Communication Technology

BPPT, Indonesia

Research Center for Informatics LIP!, Indonesia - Co-Chair

Institute of Computer-aided

Product Development

Systems, Universitat Stuttgart, Germany

Faculty of Computer Science, Indonesia University,

Indonesia

Research Center for Electrical Power and Mechatronics

LIPI, Indonesia

School of Electrical

Engineering,

Pelita · Harapan

University, Indonesia

IEEE Indonesia Section

Tampere University of Technology, Finland

Kazakh-British Technical University, Kazakhstan

University of Malaysia Perlis, Malaysia

Research Center for Informatics LIP!, Indonesia

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- ----.. ---- -- , - --- --, __________ ,. ___ ---- -... - ...

-rr----·---· ...

Jakarta, Indonesia, 19-21 November 2013

Organizing Committee

Ana Hadiana

Agus Subekti

Driszal Fryantoni

Evandri

Ferdian Yunazar

Hilman F. Pardede

Rifki Sadikin

Eyi

K.

Habibie

Lintang D. Febriani

Reviewers

Andria Arisa!

Ana Hadiana

Anto S. Nugroho

8ernadetta K wintiana Ane

Didi Rosiyadi

Estiko Rijanto

John E. 8atubara

Laksana T. Handoko

Mahmud M. Hasan

Rifki Sadikin

Driszal Fryantoni

Ferdian Yunazar

Deni Toruan

Muhammad

R.

K.

Aziz

Koredianto Usman

8ambang Sugiarto

Rafi

Ullah

Nasrullah Armi

Yusuf N ur Wijayanto

Devi M unandar

Research Center for Informatics LIPI, Indonesia - Chair

Research Center for Informatics LIPI, Indonesia - Co-Chair

Research Center for Informatics LIPI, Indonesia

Adrian 8 . Suksmono

Agus Subekti

Chan 8asaruddin

Esa Prakasa

Hilman F. Pardede

Kuncoro Wastuwibowo

Moncef Gabbouj

Mohd

Y.

Mashor

Roberto Togneri

Evandri

Irma Zakia

I Gusti 8.8. Nugraha

Hurriyatul Fitriyah

Riries Rulaningtyas

Hanung Adi Nugroho

Indra Sakti

(6)

Jakarta, Indonesia, 19-21 November 2013

Contents

[Plenary Speaker] Biological ,. blind-tracking'' task through artificial llltclligcnce

1

Md. Mahmud Hasan

[Plenary Speaker] KE AS AFFECTIVE DESIGN I\.JETHODOLOGY

7

[Plenary Speaker] What do lVlachine Learning and Particle Swarm Optimization

have to do with Content-Based Search in Large· l'dultimedia Databases?

15

Monce/ Gabbouj

[Plenary Speaker] Remote Sensing: Searching Better Accuracy for Greenhouse

Gasses

ｾＧＱｯｮｩｴｯｲｩｮｧ＠

17

Bernadetta Kwintiana Ane

Fuzzy-Based Spectrum Handoff and Channel Selection for Cognitive Radio Networks

23

Ejaz Ahmed, Liu Yao, Salman Ali, Muhammad Shiraz and Abdullah Gani

PEGAS: Pru·titioned GTS Allocation Scheme for IEEE 802.15.4 Networks

M. Udin Harun Al Rasyid, Lee Bih-Hwang and Amang Sudarsono

Goal Programming based l'vlulti-criteria Decision-making for Distributed Denial of

29

Service Attacks in Wireless Sensor Networks

33

Rolla Alomary and Salman Khan

Proposed Analysis of Dust and Sand Storms Effects on Satellite Links in Saudi Arabia

39

Abdulaziz Alyami, Kamal Harb, Samir Abduljauwad, Omair Butt, Abdullah, and Amin

Suhar-jono

Performance Analysis of Coordinated Distributed Data Scheduling Schemes in

Wire-less Mesh Network

43

Dwi Rochma Agustiningsih, Nachwan Mufti Adriansyah and Tody Ariefianto

Hop Distances Optimization for Balancing The Energy Consumption of Multi-hop

Clustered Wireless Sensor Networks

49

Amin Suharjono, Wirawan Wirawan and Gamantyo Hendrantoro

Identification of Orchid Species Using Content-Based Flower Image Retrieval

53

Diah Harnoni Apriyanti, Aniati Murni Arymurthy and Laksana Tri Handoko

Semi Automatic Detector of Plasmodium Falcipru·um on lVlicroscope Image Based

59

Jis'Hamsir Ayub Wahab, Adhi Susanto, P. lnsap Santosa and Maesadji Tjokronegoro

Weaving Effects in l'vletamorphic Animation of Tree-like Fractal based on a Family

of lVlulti-transitional Iterated Function System Code

65

(7)

Portable Srwu·t Sorting and Grading l\lachine for Fruits Usi11g Computer Vision

71

Hadha Afrisal, Muhammad Faris, Guntur Prasetyo, Lafiona Grezelda, lndah Soesanti and

Mochammad Firdaus

Paddy Diseases Identification with Texture Analysis using Fractal Descriptors Based

011

Fourier Spectrum

77

Auzi Asfarian, Yeni Herdiyeni and Aunu Rauf

Hybrid K-1\.Ieans Clustering \Vith Region Growing Algorithm For Acute Leukemia

Blood Cells Image Segmentation

83

Farah Hanim Abdul Jabar, Waidah Ismail, Rosalina Abdul Salam and Rosline Hassan

Optimal Energy Control of DC Motor Speed Control: A Comparative Study

89

Hari Maghfiroh, Dyas Wahyunggoro and Adha Imam Cahyadi

Control of a Magnetic Levitation System Using Feedback Linearization

95

Rudi Uswarman, Adha Imam Cahyadi and Dyas Wahyunggoro

Stable Extended Predictive Control

99

Daniel Viudez-Moreiras, Angelo Raimondi, Isaias Martin and Juan Martin-Sanchez

A Robust Feedback Gains for Linear Systems with IV1ultiple Delay Components

105

Adha Cahyadi and Yoshio Yamamoto

Auto-Tuning Quadcoptcr Using Loop Shaping

111

Hilton Tnunay, Muhammad Abdurrohman, Yuliyanto Nugroho, Reka Inovan, Adha Cahyadi

and Yoshio Yamamoto

A Comparative Study of PID, ANFIS and Hybrid PID-ANFIS Controllers for Speed

Control of Brushless DC Motor Drive

117

Hidayat Tanjung, Sasongko Promonohadi, Sarjiya and Suharyanto

Adaptive Prefetching of Podcasts in a Limited Bandwidth Network

123

Windhya Hansinie Rankothge and Gihan Dias

Performance Evaluation of Coarse Time Synchronization on OFDI'vl under

Multi-path Channel

129

Suyoto, Agus Subekti and Sugihartono

Application of Extremal Optimization Algorithm to Multi-objective Topology

De-sign of Enterprise Networks

135

Salman Khan

Design and implementation of an Internet of Things based Quality Control System

141

Yuhao Deng, Haiping Zhu, Guojun Zhang and Hui Yin

Delta Encoding Based Data Compression for Coastal Radar

Nuryani and H endrawan

Comparison Proactive and Reactive Routing Protocol in l\Jobile Adhoc Network

147

Based on Ant Algorithm

153

Istikmal, Leanna Vidya Yovita and Basuki Rahmat

Whitefly {Bemisia tabaci) Calculation Based on Image Processing using Triangle

Method

159

Doddy

Tri Hutomo and Yeni H erdiyeni

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Applicatio11 Image Processing tu Predict Persouality Dasecl 011 Structure of

Hand-writing and Signature

Esmeralda Contessa Djamal, Sheldy Nur Ramdlan and Risna Darmawati

Sugarcane Leaf Color Classification in Sa*b* Color Element Composition

Hari Ginardi, Riyanarto Sarno and Tri Adhi Wijaya

Spline a11<J Color Representation for Batik Design l\Iodification

Nanik Suciati, Anny Yuniarti, Chastine Fatichah and Rizky Januar Akbar

An improvement tcclmique of fragile watermarking to assurance the data integrity

163

169

173

on vector maps

1 79

Shelvie Nidya Neyman, Fransisca Cahyono and Benhard Sitohang

A Lithium-ion Battery Modcliug For A HIL-Battery Simulator

185

Grace Pebriyanti

A Comprehensive Characterization of A Linear Deformation Sensor for Applications

in Triaxial Compression Tests

191

Riska Ekawita, Khairurrijal Khairurrijal, Hasbullah Nawir, Suprijadi Suprijadi and

Muham-mad M. Munir

Design Mini SC ADA For Furnace Induction Reactor of Kernel Coating

195

Adi Abimanyu, Sukarman Sukarman, Gina Kusuma, Jumari Jumari, Triyono Triyono and

Dwi Yuliansari Nurazizah

Breaking Through Memory Limitation in GPU Parallel Processing Using Strasscn

Algorithm

201

Pujianto Yugopuspito, Sutrino Cahya and Robertus Hudi

Experimental Study of DC l'vlotor Control via Communication Network using

l\farko-vian Jump System Approach

207

Indra Sakti, Bambang Riyanto 'Ihlaksono and Asep Najmurrokhman

Comparison the Insertion Attack Effects on Randomness Property of Dragon and

Rabbit Stream Cipher

213

Desi Wulandari, Aries Kumala, Setyo Nugroho and Santi Indarjani

Implementation of Insertion Attack on Pseudorandom Number Generator ANSI

X9.17 and ANSI X9.31 based on Statistical Distance Tests and Entropy

Differ-ence Tests

Kuni Inayah, Bondan Estuwira Sukmono, Rahmat Purwoko and Santi Indarjani

Supporting Decision ]\:faking in Situational Crime Situation using Fuzzy Association

219

Rule

225

Noor Maizura Mohamad Noor, Wan Mohd Farhan Wan Nawawi and Ahmad Faiz Ghazali

Dengue Notification System using Fuzzy Logic

231

Rajul Rosli Razak, Rosmawati Abd. Wahab, and Muhammad Hermi Ramli

Lineru·-Quadratic Cost Function for Dynamic System l\:fodelling Using Recurrent

Neural Networks

237

Erwin Sitompul

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Commodity Price Prediction Using Neural Network, Case Study: Crude Pahu Oil

Price

243

Robert Gunawan, Masayu Leylia Khodra and Harlili

A Web-Based IT Asset I'vlanagcment Application Using FHzzy Logic in Vendor

Se-lection Process

Valencia Wijaya, Ririn Ikana Desanti and Samuel Lukas

Linear Function and lnve1·se Function with \Veight Ratio for Improving Learning

249

Speed of l\lulti-layer Perceptro11s Feed-forward Neural Nct\vorks

255

Dian Andriana

Application Distribution l\:Iodel In Volunteer Computing Environmeut Using

Peer-to-Peer Torrent Like Approach

261

Yustinus Soelistio

Weighted Ontology and \Veiglited Tree Similarity Algorithm for Diagnosing

Dia-betes Mellitus

267

Sugiyanto, Widhy Hayuhardhika Nugraha Putra, Riyanarto Sarno and Mohamad Sidiq

An Empirical Study of Unethical Behavior in a Tertiary Institution in l\falaysia

273

Azhar Abd Aziz, Arwin Idham Mohamad, Anitawati Mohd Lokman and Zawiyah Mohammad

Yusof

Data Cent.er for Integrating Weather l\Jonitoring Systems

279

Purnomo Husnul Khotimah and Devi Munandar

Indonesian Hadith Retrieval System using Thesaurus

285

Ikhsan Rasyidi, Ade Romadhony and Agung Toto Wibowo

Predict Fault-Proue Classes using the Complexity of UML Class Diagram

289

Arwin Halim

Text l\fossage Categorization of Collaborative Learning Skills in Online Discussion

Using Support Vector l\fachine

295

Erlin, Unang Rio and Rahmiati

PROD1\

1

IL Performance Evaluation as SOT Data Exchange Standard

301

Bobby Suryajaya and Charles Lim

Support Vector Regression for Service Level Agreement Violation Prediction 307

Ahmad Fadzil M Hani, Irving Vitro Paputungan, and M Fadzil Hassan

A Collaborative Portal for Integrating Resources in Grids

313

Al Parisi and Irving Paputungan

Clustering of ERP Business Process Fragments

319

Riyanarto Sarno, Hari Ginardi, Endang Wahyu Pamungkas and Dwi Sunaryono

Integration of Genetic And Tahu Search Algorithm Based Load Balancing for

Het-erogenous Grid Computing

325

Irfan Darmawan, Kuspriyanto, Yoga Priyan and Ian Joseph M

Multi Agents based Traditional l\:Iarket Customers Behavior Design

331

Purba Daru Kusuma and Azhari Sn

(10)

Decision lVlining for l\lulti Choice Workftow Patterns

337

Riyanarlo Sarno, Putu Linda I. Sari, Hari Ginardi, Dwi Sunaryono and Imam Mukhlash

Accelerating Gcuctic Schema Processiug Through Local Search

343

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Paddy Diseases Identification with Texture Analysis

using Fractal Descriptors Based on Fourier Spectrum

Auzi Asfarian, Yeni Herdiyeni

Computer Science Department Bogor Agricultural University

Bogor, Indonesia

[email protected], [email protected]

Abstract-The efforts to increasing the quantity and quality

of rice production are obstructed by the paddy disease. This research attempted to identify the four major paddy diseases in Indonesia (leaf blast, brown spot, bacterial leaf blight, and tungro) using fractal descripton to analyze the texture of the lesions. The lesion images were extracted manually. The descripton of 'S' component of each lesion images then used in classification process using probabilistic neural networks. This techniques achieved at least 83.00°/e accuracy when identifying the diseases. This method has a potential to be used as one of the

feature if it combined with other features, especially when two

diseases with relatively same color involved.

Keywords-paddy disease; fractal descriptors; texture analysis.

I. INTRODUCTION

The efforts to increase the quantity and quality of rice production to satisfy the increasing needs of rice in Indonesia experienced several obstacles, one of which is the attack of the diseases on paddy fields. Indonesian Directorate General of Food Crops [4] stated that during the period of October 2011 to March 2012, 80,096 hectares of paddy fields exposed to attach by three major paddy diseases in Indonesia: tungro, leaf blast, and leaf blight. To control these diseases and to minimize the impacts of the attacks, the diseases must be identified quickly. Unfortunately, experts who are able to identify the diseases are often unavailable in some region [13] . Computer vision is a potential solution to tackles this problem. One way to identify the diseases in plants is by observing the physical changes {diseases spots or lesions) caused by chemical changes in the sick plants [ 1

OJ.

The

images of these spots can be processed and used to recognize

the diseases quickly, easily, and inexpensively [ 13]. This method also nondestructive [2] and the results are consistent. This method involves the extraction the features of the said disease lesion.

The common paddy lesion features are the texture, the color, the position, or the size of spots or lesions [7]. Some research combined more than one of these features. For example, [l] used the texture, color, and shape to recognize blast, sheath blight, and brown spot, the three major rice diseases in Sri Lanka, with 70% accuracy. [15] used a color features {e.g. boundary color and spot color) to recognize

77

Aunu Rauf, Kikin Hamzah Mutaqin

Plant Protection Department Bogor Agricultural University

Bogor, Indonesia [email protected]

blast, brown spot, and narrow brown spot diseases and achieved 87.5% accuracy.

[6] proposed a new technique to analyze the texture using fractal descriptors based on image Fourier spectrum. When tested to four different datasets {Brodatz, USPTex, OuTex, and plant leaves), this method is more accurate and faster than any other fractal descriptor estimation techniques.

This research attempted to identify the four major paddy diseases in Indonesia using fractal descriptors proposed by [6] and assess the perfonnance of said method. The four diseases are leaf blast (Pyricularia orizae), brown spot

(Helminthosporium oryzae), bacterial leaf blight

(Xanthomonas oryzae), dan tungro (tungro bacillifonn virus).

Probabilistic Neural Networks {PNN) was chosen as the classifier because its good results in classifying plant diseases [14] and its fast process [11] which is necessary in mobile environment that will be used when the system is ready to implemented.

II. COMMON PADDY DISEASES IN INDONESIA

The Directorate General of Food Crops, Ministry of Agriculture of the Republic of Indonesia, routinely monitors some dangerous diseases on paddy crop. Table 1 presents the data on the size of the six major pests attacks on the rice fields. Three diseases that are on the table along with a brown spot disease were used in this study. The sample image of the infected leaves and lesions are presented in Fig l.

TABLE I. TuE MAJOR PADDY PESTS IN INDONESIA AND THEIR AREA OF

A IT ACK

No Pest Area of Attack (ha)

l Rice Stem Borers 57,875

2 Brown Planthopper 11,351

3 Rats 54,300

4 Tungro 4,994

5 LeafBlast 31,383

6 Bacterial leafblight 43,719

[image:11.598.288.554.289.695.2]

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Fig I. The images of 4 paddy diseases used in this research and its leison

Leaf Blast (Pyricu/aria grisae)

Leaf blast (LB) caused by Pyricularia grisae, a fungal pathogen. A blackish, rhombus-shaped, brown spot with a white colored central patches will appears at the leaves of the sick plan. Leaf blast is difficult to control because it's ability to forms pathogen strains quickly [12].

Brown Spot (Helminthosporium oryuze)

Brown spot (BS) caused by the fungus Helminthosporium oryzae. This disease can cause death in young rice plants and reduces grain quality. Leaves of the rice plants affected by this disease will have an oval shaped or round shaped brown colored patches about the size of sesame seeds [12].

Bacterial Leaf Blight (Xanthomonas campestris)

Bacterial leaf blight (BLB) is a disease caused by the bacterium Xanthomonas campestris. This disease is widespread and lowering the yield to 36%. HBD disease produces two characteristic symptoms: the crackle and blight. Crackle is a symptom that occurs in old plants <30 days. Leaves becomes grayish, folded, and rolled. In severe circumstances, the entire leaf curl, wilt, and die. Symptoms begin with a speckling of gray (yellow) on the edge of the leaf. During its development, the symptoms will be expanded, forming blight, and finally dried leaves [12] .

Tungro (Tungro Bacilliform Virus)

Tungro (TG) is one of the important diseases in rice. It is very destructive. It caused by tungro bacilliform virus (RTBV). Depends on the phase of the infected plants, tungro can cause 5-70% yield loss. The younger the plants is, he loss caused by tungro will be greater. Tungro attacks prominent symptom is discoloration of leaves and stunted plant growth. Diseased plant leaf color varies from slightly yellow to orange. The typical symptoms are determined by the level of resistance of varieties, environmental conditions, and the growth stage when the plants infected [12].

From above description, we already seen the general characteristic of each disease. In practice, diagnosing a disease visually involves more than one lesion characteristic i.e. the location of the symptoms, the spots or lesions shape, the size of the spots or lesions, and the color of the spots or lesions [18]. In this research, we only focused on descripting the color

78

texture of the disease lesions. This, however, does not mean that color texture is enough to differentiate the diseases. Some diseases may have a similar color and can only be differentiate by using the shape. The most obvious example of this kind of disease are brown spot (Helminthosporium oryzae) and narrow brown spot (Cercosporajanseana).

III. FRACTAL DESCRIPTORS

Mandelbrot [8] stated that many objects in nature has a fractal properties: self-similarity and complexity. The complexity of the value, which is usually expressed by the fractal dimension, can be taken from any objects that exist in nature and this value can be used to describes a natural object [9]. In fractal dimension, an object can have a fractional dimension, not only l like a line or 2 like a plane.

According to [9], the fractal dimension is not affected by the distortion due to projection and can provide information about the anisotropy and estimate the gradient of the surface texture of the object. There are several methods to estimate the fractal dimension, like Bouligand-Minowski, Brownian Motion, box-counting, multifractal spectrum, lacunarity measure, regularization dimension, and a dimension variation.

The fractal dimension, however, only consists of one number. In many case, identifying things by only one number is not enough. To overcome this limitation, the concept of fractal dimension then expanded into fractal descriptor, which contains a set of values derived from fractal dimension calculation process to become an image features. Bruno et al.

[3] suggested that the fractal descriptors can be used to characterize the image of the main characteristics, such as texture, contour, and shape.

This research used fractal descriptor method developed by [6]. [6] proposed a method for applying texture analysis using fractal descriptors based on Fourier spectrum. A Fourier transformation applied to the image, and then the power spectrum of each frequency ring are. computed. When presented in bilog form, the slope of this curve are the fractal dimension of the image.

One more process required to get the fractal descriptors from the image. In order to get richer descriptors of the image, [3] and [6] used a multiscale analysis to the whole curve. Based on empirical results, [6] opted to use scale-space analysis developed by Witkin [16] by using various scale of k,

where k is the standard variation of the Gaussian kernel used in scale-space analysis. As showed in [6], these descriptors are able to differentiate textures in various database with better results than other fractal descriptors techniques.

IV. PROBABILISTIC NEURAL NETWORKS

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Brown Spot Leaf Blast

[image:13.597.10.555.27.526.2]

Bacterial Leaf Blight Tungro

Fig 2. Some of the images of sick leaves used in the research

ｾｊｉ［Ｎ｟Ｌ＠ .. ｾ＠ . 1 ... 4<"'-... I

...

ｾ＠

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.

ﾷｾ＠

...

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...

,... ·:

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.

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:

-

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ｾ

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ﾷ＠

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(a) {b)

(c)

(d)

Fig 3. The extracted lesion images. (a) brown spot, (b) bacterial leaf blight, (c) leaf blast, and (d) tungro even up to 200,000 times faster [11] as it only needs one

iteration of training process [5]. [14] used PNN to classify plant diseases with accuracy over 95%.

V. RESEARCH METHOD

First, the images of leaves for each disease were collected. Then, the lesions were cropped manually. Each of these lesion images then converted to HSV color space. We extracted the saturation components from the images. Because of the differences on lighting condition, we done histogram equalization [ 17] first to the images. Then, we sharpened the images using Laplacian filters.

The fractal descriptors then extracted from each lesion images using method proposed by [6]. These values then used in diseases classification process using PNN. The training and testing data were splitted using 5-fold cross validation. The

79

results were presented using confusion matrix for further analysis.

VI. RESULTS AND DISCUSSION

In this research, 40 JPEG images of sick paddy leaves were obtained with each disease has 10 images. Some of these images were taken in paddy field in Laladon and Cipanas, West Java, Indonesia. Unfortunately, there are only few samples available for leaf blast diseases. To compensate this condition, we took some other images from the Internet that have different size and quality than our images.

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Fig. 4. depicts the averaged values of the descriptors of each class. The descriptors then used in classification process using PNN and 5-fold cross validation. The classification results are presented in confusion matrix (Table 2). The overall accuracy are 91.80% with classes accuracy as follows:

Brown spots accuracy : 92.31 % Bacterial leaf blight accuracy : 96.25% Leaf blast accuracy : 83.00% Tungro accuracy : 97.96%

TABLE II. COl\Ft:SIOI\ ｾａ＠ TRIX OF rnE CLASSIFICATIOI\ RESI.;LT

Prediction

BS BLB

LB

TG

BS 24 1 1 1

-;;; _BLB ₀ ₇₇ ₂ ₁

2

LB 0 5 83 12

u

<

_TG ₀ ₁ ₁ ₉₆

Noles:

BS : Brown spot LB : Leaf blast

c

·'-

.

Ｎ［Ｂｾｾ＠ ..

Ｇﾷｾ＠

' ':"- t.

BLB

TG

' •'

\.,;,

..

· t.· . .

ｾ＠.t: ...

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ＧＧＤｾ＠

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: Bacterial leaf blight : Tungro

- - brown spot

- bacltnal , .. , bltgl>I

-+-leaf blast --tungro

Fig 4. The average value of fractal descriptors of each disease class.

Three of four classes already has more than 90% accuracy. Leaf blast accuracy, however, only stand at 83.00%. The reasons why the leaf blast often misclassified is probably the variation of their lesion color (Fig. 5). Some lesions has a color that lighter, similar to bacterial leaf blight, while others has a darker color, similar to tungro. As the results of these 5 instances of leaf blast are misclassified as bacterial leaf blight

and 12 instances are misclassified as tungro, making it's accuracy the poorest at 83.00%.

In this case, another feature are needed to differentiate the leaf blast lesion from other diseases lesions. In this case, we have to consider another feature of the lesions the shape. A leaf blast lesions are elliptical or spindle shaped [19], different than bacterial leaf blight and tungro that has elongated shape.

80

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Fig 5. The various lesion color oflcafblast disease.

VII. CONCLUSION

We have already used the fractal descriptors method developed Bay [6] to identify 4 common paddy diseases in Indonesia with at least 83.00% accuracy. This method has a potential to be used as one of the feature if it combined with other features, especially when two diseases with relatively same color involved.

[I I

[2)

REFERENCES

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I 0.1109/ICllNFS.2009.5429828.

Brosnan T, Sun D. 2004. Improving quality inspection of food products by computer vision-a review. Journal of Food Engineering.

61:3-16 .

(3) Bruno OM, Plotze RD, Falvo M, de Castro M. 2008. Fractal dimension applied to plant identification. Information Sciences.

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