universiti putra malaysiapsasir.upm.edu.my/id/eprint/56665/1/fk 2015 36rr.pdfpersamaan model yang...

UNIVERSITI PUTRA MALAYSIA

MOHD HAMIM BIN ABDUL AZIZ

FK 2015 36

OIL PALM FRUIT BUNCHES MATURITY PREDICTION USING STANDARD DEVIATION OF COLOURS

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OIL PALM FRUIT BUNCHES MATURITY PREDICTION USING STANDARD

DEVIATION OF COLOURS

By


Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia,

in Fulfillment of the Requirements for the Degree of Master of Science

June 2015

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COPYRIGHT

All material contain within the thesis, including without limitation to text, logos, icons,

photographs and all other artwork, is copyright materials of Universiti Putra Malaysia

unless otherwise stated. Use may be made of any material contained within the thesis

for non-commercial purposes from the copyright holder. Commercial use of material

may only be made with the express, prior, written permission of Universiti Putra

Malaysia.

Copyright © Universiti Putra Malaysia

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Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfilment of

the requirement for the degree of Master of Science

OIL PALM FRUIT BUNCHES MATURITY PREDICTION USING STANDARD

DEVIATION OF COLOURS

By


June 2015

Chairman : Professor Wan Ishak Wan Ismail, PhD, PEng

Faculty : Engineering

Determination of correct maturity stage of the oil palm fruit bunches is crucial in

maximizing oil extraction rate (OER) of the fresh fruit bunch (FFB). A right

determination of the maturity stage will lead to the right time of fruit bunch harvesting

day which is only ripe fruit bunch will be harvested. Traditional inspections of oil palm

fruits to determine its degree of maturity was inaccurate and varies among different

inspectors. While current practices of oil palm maturity stage determination such thru

observation of number of loose fruits felled on the ground was destructive and time

consuming. In this research, non-destructive and real time image capturing was applied

and digital value of hue was used as a color space for image analysis. The research is

conducted to monitor the color deviation of the oil palm bunch during the ripening

process. The procedure in this study is started with real time image acquisition using

Keyence camera which will give real time reading of HSB (Hue, Saturation and

Brightness) digital number. Nine different fruit bunch from 5 years old of the oil palm

trees selected for this study and the variety of oil palm is Tenera: Elaeis Guineensis.

The hue digital value obtained from the Keyence camera real time reading will then

analyzed and correlated with mesocarp oil content and number of loose fruits to

develop a model for oil palm fruit bunch maturity stage. During image capturing, light

intensity surrounding the fruit bunch was monitored using Extech Lightmeter.

Regression analysis of linear model shows that the color deviation derived from hue

standard deviation was significant in estimating the days to harvest the fruit bunch. The

equation model obtained was y = 0.109x + 0.89 and R2 = 0.726 with y, x and R2

respectively represent hue standard deviation i.e. color deviation, estimated days to

harvest and regression squared. It was physically observed that color distribution on

surface of ripe fruit bunch is more uniform while color distribution for under ripe is

less uniform. Delayed days to harvest affected the fruit bunch to become overripe.

Overripe means the number of loose fruits and content of mesocarp’s free fatty acid

(FFA) would increase. In determination of oil content, it was found that there is a

different pattern rate of oil accumulation in different part of the fruit bunch. Overall

relationship of the average oil content with days to harvest shows a high correlation

with equation model y = 0.779x + 69.79 and R2 = 0.774 with y, x and R2 respectively

represent percentage of mesocarp oil content, estimated days to harvest and regression

squared. From the model developed will hopefully helpful in predicting the optimum

day to harvest the oil palm fruit bunch.

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Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai

memenuhi keperluan untuk ijazah Sarjana Sains

RAMALAN KEMATANGAN BUAH KELAPA SAWIT MENGGUNAKAN

SISIHAN PIAWAI WARNA

Oleh


Jun 2015

Pengerusi : Professor Wan Ishak Wan Ismail, PhD, PEng

Fakulti : Kejuruteraan

Penentuan kematangan tandan buah kelapa sawit yang betul adalah penting dalam

memaksimumkan kadar ekstrak minyak (KEM) bagi tandan buah segar (TBS).

Penentuan kematangan yang betul akan membawa kepada hari pemetikan tandan buah

yang betul dimana hanya tandan buah yang masak akan dipetik. Pemerhatian secara

tradisional buah kelapa sawit untuk menentukan kematangan buah adalah kurang tepat

dan berbeza dikalangan pemerhati. Sementara amalan penentuan tahap kematangan

buah kelapa sawit yang terkini seperti pemerhatian bilangan buah lerai yang jatuh di

atas tanah adalah merosakkan dan memakan masa. Dalam kajian ini, pengambilan

gambar yang tidak merosakkan dan pada masa sebenar diaplikasikan dan nilai digital

warna digunakan sebagai ruang warna untuk analisis imej. Kajian yang dilakukan

adalah untuk memerhati sisihan warna tandan buah sawit ketika dalam proses

kemasakan. Prosedur dalam kajian ini bermula dengan pengambilan imej secara masa

sebenar menggunakan kamera Keyence dimana akan memberi bacaan nombor digital

HSB (Hue, Saturation and Brightness) secara langsung. Sembilan tandan buah

daripada pokok yang berusia 5 tahun dipilih untuk kajian ini dan jenis kelapa sawit

adalah Tenera: Elaeis Guineensis. Nilai digital warna yang diperolehi dari bacaan terus

kamera Keyence akan dianalisis dan akan dihubungkaitkan dengan kandungan minyak

dalam mesokap dan bilangan buah lerai untuk menghasilkan model untuk tahap

kamatangan tandan buah kelapa sawit. Ketika sesi pengambilan imej, keamatan cahaya

pada tanda buah dipantau menggunakan Extech Lightmeter. Analisis regresi model

linear menunjukkan bahawa keseragaman warna yang diterbitkan dari sisihan piawai

warna adalah signifikan dalam menganggarkan hari bagi memetik tandan buah.

Persamaan model yang diperolehi adalah y = 0.109x + 0.89 dan R2 = 0.726 dengan y, x

dan R2 masing-masing mewakili sisihan piawai warna iaitu keseragaman warna,

anggaran hari untuk memetik dan kuasa dua regresi. Secara fizikalnya adalah

diperhatikan bahawa taburan warna pada permukaan tandan buah yang masak adalah

lebih seragam, manakala bagi yang kurang masak taburan warnanya adalah kurang

seragam. Kelewatan dalam memetik buah akan menyebabkan buah menjadi terlebih

masak. Terlebih masak disini adalah bermaksud bilangan buah lerai dan kandungan

asid lemak bebas akan meningkat. Dalam menentukan kandungan minyak, telah

didapati bahawa terdapat pola kadar pengumpulan minyak yang berbeza pada bahagian

yang berbeza pada tandan buah. Hubungan keseluruhan purata kandungan minyak

dengan hari untuk memetik menunjukkan korelasi yang tinggi dengan persamaan

model y = 0.779x + 69.79 dan R2 = 0.774 dengan y, x dan R2 masing-masing mewakili

peratusan kandungan minyak, hari untuk memetik dan kuasa dua regresi. Daripada

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model yang telah dihasilkan diharapkan dapat membantu dalam meramal hari yang

optimum bagi memetik tandan buah kelapa sawit.

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ACKNOWLEDGMENTS

First and foremost, I would like to thank Allah S.W.T, through His mercy, has given

me the patience, strength, determination and courage to complete this research. I am

deeply indebted to the chairman of supervisory committee, Professor Ir. Dr. Wan Ishak

Wan Ismail and to co-supervisor, Associate Professor Dr. Siti Khairunniza Bejo for

their outstanding advices and assistance throughout my study.

I would like to thank and appreciate the University Agricultural Park for giving

permission for me to my field work at UPM oil palm plantation. I would also like to

take this opportunity to thank Dr. Hudzari Razali, Dr. Muhamad Saufi Mohd Kassim

and Mr. Zakiria Ismail for their constructive suggestion and guides from time to time.

Similarly I would like to appreciate the assistance of staff of Department of Biological

and Agricultural Engineering and Department of Food and Process Engineering,

Universiti Putra Malaysia for their supports.

Very special thanks go to friends from kariah Masjid Taman Equine, Sri Kembangan,

Selangor, Malaysia, and all of my friends who directly or indirectly assist me during

the course of study. I am grateful to my family members including my parents, wife,

children, sisters, brothers, brothers in law, and parents in law; there are no words that

could describe my feeling to all of you. Thank you for your moral support,

encouragement, patience, sacrifices, love and prayers. I would like to express my

humble apology to those individuals, who helped me but may not find their names in

my narration here.

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I certify that an Examination Committee has met on 12th June 2015 to conduct the final

examination of Mohd Hamim Abdul Aziz on his Master of Science thesis entitled “Oil

Palm Fruit Bunches Maturity Prediction Using Standard Deviation of Colours” in

accordance with the University and Colleges Act 1971 and the Constitution of the

Universiti Putra Malaysia [P.U.(A) 106] 15 March 1998. The committee recommends

that the candidate be awarded the Master of Science.

Members of the Examination Committee are as follows:

Abdul Rashid Mohamed Shariff, PhD

Associate Professor

Faculty of Engineering

Universiti Putra Malaysia

(Chairman)

Rimfiel Janius, PhD

Associate Professor



(Internal Examiner)

Dato’ Mohd Marzuki Mustafa, PhD, PEng Professor


Universiti Kebangsaan Malaysia

(External Examiner)

ZULKARNAIN ZAINAL, PhD

Professor and Deputy Dean

School of Graduate Studies


Date: 22 September 2015

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This thesis submitted to the Senate of Universiti Putra Malaysia has been accepted as

fulfilment of the requirement for the degree of Master of Science. The members of the

supervisory committee are as follows:

Wan Ishak Wan Ismail, PhD, PEng

Professor



(Chairman)

Siti Khairunniza Bejo, PhD

Associate Professor



(Member)

BUJANG KIM HUAT, PhD

Professor and Dean

School of Graduate Studies


Date:

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Declaration by graduate student

I hereby confirm that:

this thesis is my original work;

quotations, illustrations and citations have been duly referenced;

this thesis has not been submitted previously or concurrently for any other degree

at any other institutions;

intellectual property from the thesis and copyright of thesis are fully-owned by

Universiti Putra Malaysia, as according to the Universiti Putra Malaysia

(Research) Rules 2012;

written permission must be obtained from supervisor and the office of Deputy

Vice-Chancellor (Research and Innovation) before thesis is published (in the form

of written, printed or in electronic form) including books, journals, modules,

proceedings, popular writings, seminar papers, manuscripts, posters, reports,

lecture notes, learning modules or any other materials as stated in the Universiti

Putra Malaysia (Research) Rules 2012;

there is no plagiarism or data falsification/fabrication in the thesis, and scholarly

integrity is upheld as according to the Universiti Putra Malaysia (Graduate Studies)

Rules 2003 (Revision 2012-2013) and the Universiti Putra Malaysia (Research)

Rules 2012. The thesis has undergone plagiarism detection software.

Signature :_____________________ Date: 21st December 2015

Name and Matric No.: Mohd Hamim bin Abdul Aziz / GS25307

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Declaration by Members of Supervisory Committee

This is to confirm that:

the research conducted and the writing of this thesis was under our supervision;

supervision responsibilities as stated in the Universiti Putra Malaysia (Graduate

Studies) Rules 2003 (Revision 2012-2013) are adhered to.

Signature: ___________________________

Chairman of Supervisory Committee: Wan Ishak Wan Ismail, PhD, PEng

Signature: ___________________________

Member of Supervisory Committee: Siti Khairunniza Bejo, PhD

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TABLE OF CONTENTS

Page

ABSTRACT i

ABSTRAK ii

ACKNOWLEDGEMENTS iv

APPROVAL v

DECLARATION vii

LIST OF TABLES xi

LIST OF FIGURES xiii

LIST OF ABBREVIATIONS

xvii

1 INTRODUCTION

1.1 Background 1

1.2 Problems Statement 2

1.3 Objectives 3

1.4 Scope of study 3

2 LITERATURE RIVIEW

2.1 Introduction 4

2.2 Ripening Process of Oil Palm Fruit Bunch 5

2.2.1 Fruit Development and Synthesize of Oil. 7

2.2.2 Fruits Skin Color 9

2.2.3 Number of Loose Fruits 10

2.3 Harvesting Interval 11

2.4 Application of Machine Vision in Agriculture 12

2.4.1 Sorting by Color 12

2.4.2 Detection of Weeds 12

2.4.3 Quality Assessment 13

2.4.4 Livestock Identification 13

2.4.5 Tractor Guidance 13

2.5 Digital Value in Agricultural Products 14

2.5.1 Indoor Machine Vision 16

2.5.2 Outdoor Machine Vision 17

2.6 Analysis of Variance (ANOVA) 17

3 METHODOLOGY

3.1 Overview 19

3.2 Duration of Field Works 19

3.3 Work Flow for Outdoor Imaging Technique 19

and Determination of Hue Value

3.4 Procedure for Outdoor Imaging Technique 21

3.4.1 White Balance 21

3.4.2 Shutter Speed 22

3.4.3 Measurement Tool 22

3.4.4 Image Registration 24

3.4.5 Measurement Window 24

3.4.6 Light Intensity Monitoring 26

3.5 Selecting the Fruit Tree and the Sample Bunch 27

3.6 Image Acquisition and HSB Reading 27

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3.7 Determination of Mesocarp Oil Content 31

3.8 Equipments for Oil Content Analysis 31

3.8.1 Digital Balancer 31

3.8.2 Oven 31

3.8.3 Soxhlet Extractor 31

3.8.4 Filter Paper 32

3.8.5 Solvent 32

3.8.6 Desiccator 32

3.8.7 Dryer Particle 32

3.9 Simulation Model Development 32

3.10 Overall Research Work Flow 33

4 RESULTS AND DISCUSSION

4.1 Introduction 36

4.2 Determination of Hue Value for Three Different Parts 36

Of Fruit Bunches

4.2.1 Conversion of Hue from Keyence Camera HSB

Digital Value 36

4.2.2 Data and Results 49

4.2.3 Statistical Results 52

4.2.3.1 Color Uniformity from Hue Analysis 52

4.2.3.2 Analysis of Variance for Hue 62

4.2.4 Overall Relationship of Color (Hue) Uniformity with

Days to Harvest. 69

4.3 Oil Content Accumulation on Three Different Parts of Fruit

Bunches. 70

4.3.1 Data and Results 70

4.3.2 Pattern of Oil Accumulation 72

4.5 Model for Oil Palm Fruit Bunches Ripeness Prediction 74

5 CONCLUSIONS AND RECOMMENDATIONS

5.1 Conclusions 76

5.2 Recommendations for Future Works 76

REFERENCES 78

BIODATA OF STUDENT 83

PUBLICATIONS 84

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LIST OF TABLES

Table

Page

2.1 Time and motion study on harvesting and collecting

of bunches.

11

4.1 (a) Hue value for each point for bunch 1.

39

4.1 (b) Hue value for each point for bunch 2.

39

4.1 (c) Hue value for each point for bunch 3.

40

4.1 (d) Hue value for each point for bunch 4.

40

4.1 (e) Hue value for each point for bunch 5.

40

4.1 (f) Hue value for each point for bunch 6

41

4.1 (g) Hue value for each point for bunch 7

41

4.1 (h) Hue value for each point for bunch 8.

41

4.1 (i) Hue value for each point for bunch 9.

42

4.2 (a) Color uniformity (STDV), number of loose fruits (LF)

and estimated days to harvest (DTH) of bunch 1

52

4.2 (b) Color uniformity (STDV), number of loose fruits (LF)


54

4.2 (c) Color uniformity (STDV), number of loose fruits (LF)


55

4.2 (d) Color uniformity (STDV), number of loose fruits (LF)


56

4.2 (e) Color uniformity (STDV), number of loose fruits (LF)


57

4.2 (f) Color uniformity (STDV), number of loose fruits (LF)


58

4.2 (g) Color uniformity (STDV), number of loose fruits (LF)


59

4.2 (h) Color uniformity (STDV), number of loose fruits (LF)


60

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4.2 (i) Color uniformity (STDV), number of loose fruits (LF)


61

4.3 (a) Significance of Hue Values for Bunch 1

62

4.3 (b) Significance of Hue Values for Bunch 2 63

4.3 (c) Significance of Hue Values for Bunch 3 63

4.3 (d) Significance of Hue Values for Bunch 4 64

4.3 (e) Significance of Hue Values for Bunch 5 65

4.3 (f) Significance of Hue Values for Bunch 6

65

4.3 (g) Significance of Hue Values for Bunch 7 66

4.3 (h) Significance of Hue Values for Bunch 8 67

4.3 (i) Significance of Hue Values for Bunch 9 67

4.4 Statistical result of oil palm bunch hue digital number

from Keyence camera

68

4.5 (a) Oil content for different part of bunch 1

70

4.5 (b) Oil content for different part of bunch 2

70

4.5 (c) Oil content for different part of bunch 3

71

4.5 (d) Oil content for different part of bunch 4

71

4.5 (e) Oil content for different part of bunch 5

71

4.5 (f) Oil content for different part of bunch 6

71

4.5 (g) Oil content for different part of bunch 7

72

4.5 (h) Oil content for different part of bunch 8

72

4.5 (i) Oil content for different part of bunch 9

72

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LIST OF FIGURES

Figure

Page

2.1 Different part of the oil palm bunch.

6

2.2 Division of outer and inner spikelet.

6

2.3 Oil Palm Fruit Morphology.

7

2.4 Pattern of oil accumulation.

8

2.5 Comparison of virescens and nigrescens bunches.

9

2.6 Relation of percent oil to mesocarp with percentage of loose

fruit and relationship of percent oil to mesocarp with

number of loose fruit.

10

2.7 A frame-grab from the computer-processing of image

data to locate weeds.

12

2.8 S-psi plot of the outline of a cow.

13

2.9 Frame-grab from the video image that is guiding a tractor.

13

2.10 Hue histogram of unripe fruit bunch

15

2.11 Hue histogram of ripe fruit bunch

15

2.12 Hue histogram of fruit bunch of intermediate ripeness.

15

2.13 Color images of a FFB taken from different sides.

16

3.1 Flowchart of procedure for outdoor imaging technique

and determination of hue value and its color uniformity.

20

3.2 White paper as a reference area for white balance setting.

21

3.3 Adjusting RGB multiplier

22

3.4 Shutter speed setting

23

3.5 Selecting type of measurement tool

23

3.6 Selecting type of color space

24

3.7 Image registration.

25

3.8 Shape of measurement window selection. 25

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3.9 Radius and coordinate of measurement area.

26

3.10 Lightmeter placed near the fruit bunch.

27

3.11 Keyence camera set.

28

3.12 Camera position and distance set up

28

3.13 HSB reading on monitor screen.

29

3.14 Valid and avoided points of measurement area.

30

3.15 Possible points of measurement area.

30

3.16 Overall development of oil palm fruit bunch ripeness model.

33

3.17 Flow chart for overall research flow.

35

4.1 HSB color system used in the Keyence camera.

37

4.2 Color ranges over degree of ripeness and hue conversion into

negative value.

38

4.3 (a) Bunch 1 on Dec/21/11

42

4.3 (b) Bunch 1 on Dec/23/11

43

4.3 (c) Bunch 1 on Dec/26/11

43

4.3 (d) Bunch 1 on Dec/29/11

43

4.3 (e) Bunch 1 on Dec/31/11

44

4.3 (f) Bunch 1 on Jan/06/12

44

4.3 (g) Bunch 1 on Jan/09/12

44

4.4 Random points of measurement area.

45

4.5 (a) Pattern of color change on bunch 1

45

4.5 (b) Pattern of color change on bunch 2

46

4.5 (c) Pattern of color change on bunch 3

46

4.5 (d) Pattern of color change on bunch 4

47

4.5 (e) Pattern of color change on bunch 5

47

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4.5 (f) Pattern of color change on bunch 6

48

4.5 (g) Pattern of color change on bunch 7

48

4.5 (h) Pattern of color change on bunch 8

49

4.5 (i) Pattern of color change on bunch 9

49

4.6 (a) Bunch 9 on Dec/23/11

50

4.6 (b) Bunch 9 on Dec/27/11

50

4.6 (c) Bunch 9 on Dec/29/11

51

4.6 (d) Bunch 9 on Dec/31/11

51

4.6 (e) Bunch 9 on Jan/3/12

51

4.7 Example for bunch 1 on how nine points of

measurement area taken randomly.

52

4.8 (a) The relationship of color uniformity with days

to harvest for bunch 1.

53

4.8 (b) The relationship of color uniformity with days


54

4.8 (c) The relationship of color uniformity with days


55

4.8 (d) The relationship of color uniformity with days


56

4.8 (e) The relationship of color uniformity with days


57

4.8 (f) The relationship of color uniformity with days


58

4.8 (g) The relationship of color uniformity with days


59

4.8 (h) The relationship of color uniformity with days


60

4.8 (i) The relationship of color uniformity with days


61

4.9 Overall relationship of color uniformity with days

to harvest.

69

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4.10 Oil content accumulation at three different part of bunch

73

4.11 Overall average of oil content

73

4.12 The model of the harvesting days of oil palm fruit bunch.

74

4.13 Relationship of color uniformity, days to harvest and mesocarp oil

content.

75

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LIST OF ABBREVIATIONS

ANOVA Analysis of Variance

CCD Charge Couple Device

DN Digital Number

DTH Days to Harvest

FC Foot Candle

FELDA Federal Land Development Authority

FFA Free Fatty Acid

FFB Fresh Fruit Bunch

HSB Hue, Saturation, Brightness

HIS Hue, Saturation, Intensity

HSL Hue, Saturation, Luminance

KEM Kadar Ekstrak Minyak

LF Loose Fruits

MPOB Malaysia Palm Oil Board

MRS Minimum Ripeness Standard

OER Oil Extraction Rate

RGB Red, Green, Blue

R2 Regression Squared

RGB Red, Green, Blue

STD Standard Deviation

TBS Tandan Buah Segar

UPM Universiti Putra Malaysia

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CHAPTER 1

INTRODUCTION

1.1 Background

As a developed nation, Malaysia is one of those countries which rely heavily on

agriculture sector as its main source of income. But nowadays, Malaysia is no longer

relying solely on agriculture as the contributor for its income. This is mainly due to its

economic diversification that took place for the last 30 odd years. In the past few

decades, Malaysia was aggressively expanding its economic activities by engaging in

other sectors like oil and gas, heavy industry, automotive, electronics, information

technology and tourism. However, only recently the government has put the focus back

to agricultural sector as one of the most important sector that would help Malaysia

achieving its 2020 visions. Agriculture plays an important role in rural population in

Malaysia. Most of the rural populations are engaged in agricultural work with oil palm

plantation dominating Malaysia total area of planted commodity crop. The total oil

palm planted area, both in West and East Malaysia, in 2011 was 5.00 million hectares.

This was a 3.0% increment against 4.85 million hectares recorded in 2010. This was

due to increase in planted area in Sarawak which saw an increase of 11.0% or 102,169

hectares. Sabah has the largest oil palm planted area with 1.43 million hectares or

28.6% of total oil palm planted area, followed by Sarawak with 20.4% of total oil palm

planted area.

Palm oil is the most widely traded oil in the world. The palm oil is widely used for the

production of bio-diesel, cosmetics, food product, pharmaceutical and etc. In 2011, its

exports reached almost 39.04 million metric tons of which 46% of world’s total

production came from Malaysia followed by Indonesia as the world second largest

palm oil exporter. However, Indonesia is still the largest producer of palm oil in the

world. Similar trend in export of palm kernel oil was recorded in the same year, with

the volume reaching 1.17 metric tons.

With the encouragement from the world market demand, Malaysia has allocated a huge

amount in research and development on oil palm industry. The establishment of MPOB

(Malaysia Palm Oil Board) a special body that responsible in R&D and any matter

regarding national palm oil industry is further evidence of the seriousness of the

government in this area. UPM (Universiti Putra Malaysia, formerly known as

Agricultural University of Malaysia) is another organization that is highly committed

on palm oil R&D.

With strong government backing, many states of the art technologies have been

introduced in recent years. The introduction of technology is inevitable in order for

Malaysia to compete and meet the demand of this robust global market. As a result,

many types of machines were invented such as CANTAS by MPOB, oil palm loose

fruit picker, robot arm for harvesting purpose, machine vision for bunch ripeness

determination and etc.

Color is one of the most visible indicators used to determine the maturity of

agricultural fruits, and is applicable also to oil palm fruit bunches. In order to automate

the harvesting operation of oil palm fruit, an artificial vision system which replaces the

human eye for recognizing mature fruit bunches is needed. Color is considered a

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fundamental physical property of agriculture products. Application of vision system in

agricultural produces is difficult and challenging. This is due to the uncertainty in

shape, color and other physical properties of agricultural fruits.

Currently, the maturities of fruit bunches are determined by its color, number of loose

fruits felled on the ground and percentage of oil content. If fruit bunches are harvested

too early or unripe, it may not reach optimum oil content when processed. If they are

harvested too late, the fresh fruit bunches (FFB) become overripe, thus produced oil

with high acidity of Free Fatty Acid (FFA) during mill extraction process and resulted

in low quality of oil (Azis et al., 1984; Kaida et al., 1996; Wan Ishak et al., 2000).

Therefore, a parameter which can be used to determine the level of maturity of fruit

bunches with respect to the optimum oil content is needed. Machine vision technology

had been used to inspect and grade FFB of an ordinary Elaeisguineensis in categorizing

FFB ripeness. The optical properties of an oil palm FFB with it maturity status as ripe,

unripe or overripe will be determined through its oil content measurement (Razali et

al., 2009). Ishak W.I.W and Hudzari R.M (2010) developed color simulation

forecasting model for oil palm fruit bunch ripeness in real time environment.

Therefore, this thesis presents the simulation of fruit bunches maturity by adding

another color characteristic of ripening process of fruit bunch which is the color

uniformity of the whole surface of the fruit bunch. The experiment was conducted on

fruit bunch in real time growth condition at oil palm plantation. The fruitlets of oil

palm were plucked on the same day as the images of fruit bunches were being captured

using Keyence Camera Vision. The fruitlets samples were then brought to the

laboratory for oil content analysis.

1.2 Problems Statement

Determination of ripe fruit is important to ensure only ripe or fully ripe fruit bunches

are harvested. These ripe bunches are sent to the oil mill for processing. Any unripe

fruits supplied to the oil palm mill will affect the quality and quantity of oil extraction.

There are many researches that have been conducted in search of possible techniques to

gauge the maturity of the fruit bunch. Azis (1973) introduced floatation technique,

Turner and Gilibanks (1974) relate the maturity of the fruit with number of detached

fruits from the bunch, while Kaida and Zulkifly (1992) found that there is a relationship

with microwave frequency with moisture content of the fruits. However, all of the

techniques mentioned are destructive and time consuming.

One destructive technique which requires the workers to injure the fruit bunch using

harvesting chisel to ensure the ripeness of the fruit bunch is due to their inability to see

clearly the color of fruit bunch which is high on the tree. Another harvesting technique

that has been practice in the palm oil plantation is the fruit bunches bunch will be

harvested when the workers see around 5 to 10 loose fruits felled on the ground. The

problem with this technique is the worker might not able to see or count the number of

loose fruits felled on the ground. This technique is prone to error in judgments and

inconsistencies. The worker also did not bother to collect all the loose fruits felled on

the ground.

Color is the most common criteria used to measure the ripeness of all type of fruit

including oil palm fruit bunch. Therefore, an accurate human inspection is crucial to

determine the ripeness of the fruit. Unfortunately, each workers doing harvesting may

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have their own inspection decision vary from each other. This will result in

inconsistent maturity or ripeness of the fruit bunch harvested.

Most of the previous researches done on vision system were applied on controlled

lighting environment. M.H Razali et al., (2011) developed an outdoor image based

model for estimation of fruit ripeness. The model relates surface color of the fruit

bunch with oil content. However, the color of the fruit bunch surface might vary

slightly from one tree to another or even from one bunch to another. This is possibly

due to change of environmental condition. For example, if we compare two different

ripe bunches; the color of the fruit bunch surface of the two might differ slightly. From

our observation, fruit bunch that was directly exposed to sunlight will have brighter

reddish orange color than fruit bunch that grow under the shade. Therefore, the purpose

of this research is to study another characteristic of the fruit bunch to refine the

maturity identity of the fruit bunch in outdoor condition. In this study, a color

measurement tool available in Keyence camera was applied on the color appearance of

the whole exposed fruit bunch skin surface. It was reported that the ripening of the fruit

bunch are not uniform which usually begins from the apical section, and gradually

spreading toward equatorial and basal sections of the bunch (Kaida and Zulkifly,

1992). This characteristic of the fruit bunch maturing surely will result in different

pattern of color distribution appeared during ripening process. The different pattern of

color distribution was expected to have a close relationship with stage of fruit bunch

maturity.

1.3 Objectives of the Study

The main aim of this study is to develop a model for optimum day of harvesting the

fruit bunch based on the color uniformity and mesocarp oil content correlation. The

specific objectives of the study are as follows:

1. To develop the procedure for outdoors imaging technique of machine vision

for oil palm fruit maturity inspection.

2. To determine the hue value of each three (apical, middle, and basal) part of the

fruit bunch surface and analyze it pattern of color uniformity.

3. To determine the oil content of the oil palm fruits at different stage of maturity

from three different part of the fruit bunch.

1.4 Scope of the Study

This study focused on the determination of color deviation of the fruit bunches. The

images were captured by using Keyence Camera CV-3001. The type of oil palm fruits

being studied in this research is of nigrescens type and from the tenera variety (hybrid

between the dura and pisifera) which is mostly planted in Malaysian oil palm

plantations. All experiments were carried out for 5 years old tree only. During session

of image capturing, one fruitlet from each part was plucked for oil content analysis.

The oil content of the mesocarp is percentage of oil to dried mesocarp. The monitoring

period of each bunches was start from approximately two weeks before ripe till ripe.

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REFERENCES

Abdullah, M. Z., Lim, C. G., Mohamed, A. M. D., Noor, M. A. M., Color vision

system for ripeness inspection of oilpalm Elaeisguineensis. J. Food

Process. Preservation 2002, 26,213–235.

Abdullah, M. Z., Fathinul-Syahir, A. S. and Mohd-Azemi, B. M. N. 2005. Automated

inspection system for colour and shape grading of Starfruit (Averrhoa

carambola L.) using machine vision sensor. Transactions of the

Institute of Measurement and Control. 27: 65-87.

Afshin. K., Johari E., Haniff H., Desa A., Farah S. 2011. Palm Oil Quality

Monitoring In The Ripening Process of Fresh Fruit Bunches.

International Journal of Advanced Engineering Sciences and

Technologies. Vol No. 4, Issue No. 1, 026-052.

Ai Tan Y., Kum Wan L, Chak Khiam L. and Kum Sang L. Imaging technique for

quantification of oil palm fruit ripeness and oil content. Eur. J. Lipid Sci.

Technol. 2010, 112, 838–843

Åstrand, B., and Baerveldt, A.-J., 2002. An agricultural mobile robot with vision-

based perception for mechanical weed control. Journal Autonomous

Robots, 13(1), 21–35.

Azis A.A, 1990. A Simple Floatation Technique to Gauge Ripeness of Oil Palm

Fruits and Their Maximum Oil Content. In: Proc. Of 1989,

International Palm Oil Development Conference, PORIM, Kuala

Lumpur, p.87 – 91.

Azis A. A., Mohamadiah Banjari, W Zailan Rosnah Mat Soom, W Omar and Chen

Kin W. ( 1984). Ripeness Standard: Any sign of Loose Fruit and with One

Loose Fruit per Bunch as the Minimum Standard. Proceeding Of The

Symposium On Impact Of The Pollinating Weevil On The Malaysian Oil

Palm Industry (pp. 165-176). Kuala Lumpur. Palm Oil Research Institute

of Malaysia .

Balasundram, S. K., Robert, P. C., Mulla, D. J., Relationshipbetween oil content and

fruit surface color in oil palm (ElaeisguineensisJacq.). J. Plant Sci. 2006,

1, 217–227.

Billingsley, J., and Schoenfisch, M., 1996. The successful development of a vision

guidance system for agriculture. Computers and Electronics in

Agriculture, 16(2), 147–163.

Choi K, LeeG., Han Y.J., Bun J.M. (1995), Tomato Maturity Evaluation Using Color

Image Analysis, Transaction of the ASAE, 38 (1):pg171-176.

Choong, T. S. H., Abbas, S., Shariff, A. R., Halim, R., et al.Digital image processing

of palm oil fruits. Int. J. Food Eng.2006, 2, Article 7, 1–4 [12]

© COPYRIG

HT UPM

79

Gonzalez, R. C., Woods, R. E., Digital Image Processing, 3rdEdn.

Prentice-Hall, Inc., Upper Saddle River, New Jersey,USA, 2007.

Corley, R.H.V. Syed, R.A., Law, I.H. (1982). Insect Pollination Of Oil Palm:

Introduction, Establishment And Pollinating Efficiency Of Elaeidobius

Kamerunicus In Malaysia. The Planter, Kuala Lumpur. 58:547.

Hamim AA, Ishak WIW, Hudzari RM (2010) “Machine Vision for Oil Palm Growth

Stages Recognition-A Review”, Proceeding of 3rd National Intelligent

Systems and Information Technology Symposium ISITS 2010), 13th – 14th

October 2010, Institute of Advanced Technology (ITMA), UPM, Serdang,

Selangor.

HITAM, A; DARUS, A and WENG, C K (1999). Systems approach to mechanization

in oil palm plantations. Proc. of the 1999 PORIM International Palm Oil

Congress – Emerging Technologies and Opportunities in the Next

Millennium (Ariffin Darus; Chan, K W and S S R S Alweeeds.).

PORIM, Bangi. p. 325-336.

Hoong, H W and Donough,CR (1998). Recent trend in oil palm oil extraction rate

(OER) and kernel extraction rate (KER) in Sabah. Seminar on Oil Palm

Plantation Management. Sandakan, International Society of Planters (ISP)

Kuala Lumpur.

Idris O., M.Ashhar K., M.Haniff H. and M. Basri W. (2003), Colour Meter for

Measuring Fruit Ripeness, MPOB Information Series (pp. 195), ISSN 1511-

7871

Ishak WIW, Hudzari RM (2010): Image Based modeling for oil palm fruit maturity

prediction. J. Food, Agric. Environ., 8(2): 469-476.

Kaida Khalid, Zahariah Zakaria and Wan Daud Wan Yusof, (1996), Variation Of

Dielectric Properties Of Oil Palm Mesocarp With Moisture Content And Fruit

Maturity at Microwave Frequencies, Elaeis Vol. 8 No. 2, December 1996, Pp.

83—91.

Kaida Khalid and Zulkifly Abbas (1992), A Microstrip Sensor for Determination of

Harvesting Time for Oil Palm Fruits, Journal of Microwave Power and

Electromagnetic Energy, Vol.27 No.1, 1992

Keyence Corporation, (2006a), Super High Speed Digital Machine Vision CV-3001

Series User‘s Manual, E CV-3001 0058-2

Keyence Corporation, (2006b), Super High Speed Digital Machine Vision CV-3001

Series User‘s Manual, V30-WW-C-E 0046-1 000547

Khaik C.O., Sau K.T., Hun T. K., And Augustine S.H.O. (1985), Fatty Acid

Synthesis In The Oil Palm (Elaeis Guineensis): Incorporation Of Acetate By

Tissue Slices Of The Developing Fruit, Lipids, Vol. 20, No. 4 .

© COPYRIG

HT UPM

80

K. Sunilkumar and D. S. Sparjan Babu. Surface color based prediction of oil content

in oil palm (Elaeis guineensis Jacq.) fresh fruit bunch. African Journal

of Agricultural Research Vol. 8(6), pp. 564-569, 25 February,

2013.

McCarthy, C. L., Hancock, N. H., and Raine, S. R., 2008. On-the-go machine vision

sensing of cotton plant geometric parameters: first results. In

Billingsley, J., and Bradbeer, R. S. (eds.), Mechatronics and

Machine Vision in Practice. New York: Springer-Verlag, pp. 305–

312.

Meftah Salem, M. A., Abdul Rashid, M. S., Helmi, Z. M. S., Osama, M. B. S. and

Omar, M.E. 2008. Oil palm fruit bunch grading system using red, green

and blue digital number. Journal of Applied Sciences 8(8): 1444-

1452. M.H.Harun andM. R.M.Noor, “Fruit set and oil palm bunch

components,” Journal of Oil Palm Research, vol. 14, no. 2, pp.

24–33, 2002.

Mohd. Hudzari Razali, Wan Ishak Wan Ismail, Abd. Rahman Ramli, Md. Nasir

Sulaiman and Mohd. Haniff B. Harun. African Journal of Agricultural

Research Vol. 6(7), pp. 1823- 1830, 4 April, 2011.

N. M. A. Nik Haron, Z. Abdul Halim, J. Mohd Saleh, H. Ibrahim, K. Musa, and N.

Fadzilah. White LED Illumination for Improving Outdoor Image Analysis for

Palm Oil Fruit Ripeness Detection. ICACSIS 2012, ISBN: 978-979-

1421-15-7 95.

Osama Mohammed Ben Saeed, Sindhuja Sankaran, Abdul Rashid Mohamed Shariff,

Helmi Zulhaidi Mohd Shafri, Reza Ehsani, Meftah Salem Alfatni, Mohd

Hafiz Mohd Hazir “Classification of oil palm fresh fruit bunches

based on their maturity using portable four-band sensor

system. Computers and Electronics in Agriculture 82 (2012) 55–60

Qi Wang, Hui Wanga, Lijuan Xie, Qin Zhang. Outdoor color rating of sweet cherries

using computer vision, Computers and Electronics in Agriculture 87 (2012)

113–120

Rashid Shariff, Nor Aizam Adnan, Radzali Mispan, Shattri Mansor, Rohaya Halim

and Roop Goyal (2004), Correlation Between Oil Content and DN

Values, Department of Biological and Agriculture, Universiti Putra

Malaysia, GISdevelopment.net

Rajanaidu N. and Tan B.K. (1983), Variability of fatty acid composition (fac) within

bunches in the oil palm elaeis guineensis. Oleagineux, 38: 581-584.

Rajanaidu N, Abdul Azis Ariffin, B.J. Wood and Sarjit Singh, (1987). Ripeness

Standards And Harvesting Criteria For Oil Palm Bunches, Proceeding

of International Oil Palm Conference Agriculture, Kuala Lumpur,

Malaysia.

© COPYRIG

HT UPM

81

Rao, V, Soh, Ac, Corley, Rh V, Lee, C H, Rajanaidu, N, Tan, Y P. Chin, C W, Lim, K

C, Tan, S T, Lee, T P And Ngui, M (1983), A Critical Re-Examination Of The

Method Of Bunch Analysis In Oil Palm, Breeding. PORIM Occasional Paper

No.9.

R.H.V. Corley and P.B. Tinker, The Oil Palm, Fourth Edition 2003.

Razali M.H., Wan Ismail W.I., Ramli A.R. and Sulaiman M.N.(2008), Modeling of Oil

Palm Fruit Maturity for the Development of an Outdoor Vision System,

International Journal of Food Engineering, Volume 4, Issues 3, Article 5.

Razali M.H., Wan Ismail W.I., Ramli A.R., Sulaiman M.N. and Harun M.H. (2009),

Development of Image Based Modeling for Determination of Oil Content and

Days Estimation for Harvesting of Fresh Fruit Bunches, International Journal

of Food Engineering, Volume 5, Issues 2, Article 12.

Said Ismail, B.J. Wood and Tan Swee Tian (1983), An Investigation Of The Oil Palm

Bunch Analysis Technique As A Guide To Actual Oil Content. Proceeding of

Symposium on Impact of Pollinating Weevil on The Malaysian Oil Palm

Industries. Kuala Lumpur, Malaysia.

Sambanthamurthi R, Tan YA, Chang KC, Proceedings of the National Seminar on

Opportunities for Maximizing Production Through Better OER and O

shore Investment in Palm Oil 14±15, Malaysia; p. 199±207.1998

Simulation modeling (n.d.). In Wikipedia. Retrieved January 10, 2015, from

https://en.wikipedia.org/wiki/Simulation_modeling.

S.k Balasundram, P.C Robert, D.J Mulla. 2010. Relatioship Between Oil Content and

Fruit Surface Color In Oil Palm (Elaeis guineensis Jacq.) Journal of Plant

Science 5 (3): 298- 308, 2010.

Slamet, R., Mohd. Marzuki, M., Aini, H. and Azman, H. 2007. Papaya fruit grading

based on size using image analysis. ICEEI2007, Bandung, Indonesia.

Sojodishijani, O., Ramli, A.R., Rostami, V., Samsudin, K., Saripan, M.I., 2010. Just-

intime outdoor color discrimination using adaptive similarity-based

classifier. IEICE Electronics Express 7, 339–345.

Turner, P.D and Gillibanks, R.A. (1974). Oil Palm Cultiva-tion and Management.

United Selangor Press, Kuala Kum-pur, P.478 - 485

Wan Ishak W. I. and Lim B.H. (1998), Colour Camera Vision System For Mechanical

Grading and Sorting Of Banana. Genting Highland, Malaysia, November 23-

25 1998. Proceedings Of ‘The First National Banana Seminar’. Pg 148-156.

Wan Ishak W. I., Mohd. Zohadie B., A.Malik A.H. (2000), Optical Properties for

Mechanical Harvesting of Oil Palm. Journal of Oil Palm Research, Vol. 11,

No. 2, pp. 38-45.

https://en.wikipedia.org/wiki/Simulation_modeling

© COPYRIG

HT UPM

82

Wan Ishak W.I. (2008), Semburan racun rumpai automatik. Ruangan Mega AGRO,

Utusan Malaysia, Mukasurat 10, Jumaat 22 Ogos 2008.

Wan Ishak Wan Ismail, Nur Lailina Makhtar and Mohd. Hudzari Razali (2009),

“Camera vision to predict oil palm FFB crop maturity” 3rd International

Engineering Convention (IntEC 2009). Damascus, Syria.

Yud RC, Kuanglin C, Moon SK (2002). Machine Vision Technology for Agricultural

Applications, Computers and Electronics in Agriculture, Vol.36 (1), pp.173-

191.

Zhang, Z.,Kodagoda, S., Ruiz,D., Katupitiya, J., andDissanayake,G., 2008.

Classification of bidens in wheat farms. In Proceedings of Mechatronics

and Machine Vision in Practice, 2008, pp. 505–510.

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