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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
MOHD HAMIM BIN ABDUL AZIZ
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
MOHD HAMIM BIN ABDUL AZIZ
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
MOHD HAMIM BIN ABDUL AZIZ
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
Faculty of Engineering
Universiti Putra Malaysia
(Internal Examiner)
Dato’ Mohd Marzuki Mustafa, PhD, PEng Professor
Faculty of Engineering
Universiti Kebangsaan Malaysia
(External Examiner)
ZULKARNAIN ZAINAL, PhD
Professor and Deputy Dean
School of Graduate Studies
Universiti Putra Malaysia
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
Faculty of Engineering
Universiti Putra Malaysia
(Chairman)
Siti Khairunniza Bejo, PhD
Associate Professor
Faculty of Engineering
Universiti Putra Malaysia
(Member)
BUJANG KIM HUAT, PhD
Professor and Dean
School of Graduate Studies
Universiti Putra Malaysia
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)
and estimated days to harvest (DTH) of bunch 2
54
4.2 (c) Color uniformity (STDV), number of loose fruits (LF)
and estimated days to harvest (DTH) of bunch 3
55
4.2 (d) Color uniformity (STDV), number of loose fruits (LF)
and estimated days to harvest (DTH) of bunch 4
56
4.2 (e) Color uniformity (STDV), number of loose fruits (LF)
and estimated days to harvest (DTH) of bunch 5
57
4.2 (f) Color uniformity (STDV), number of loose fruits (LF)
and estimated days to harvest (DTH) of bunch 6
58
4.2 (g) Color uniformity (STDV), number of loose fruits (LF)
and estimated days to harvest (DTH) of bunch 7
59
4.2 (h) Color uniformity (STDV), number of loose fruits (LF)
and estimated days to harvest (DTH) of bunch 8
60
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4.2 (i) Color uniformity (STDV), number of loose fruits (LF)
and estimated days to harvest (DTH) of bunch 9
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
to harvest for bunch 2.
54
4.8 (c) The relationship of color uniformity with days
to harvest for bunch 3.
55
4.8 (d) The relationship of color uniformity with days
to harvest for bunch 4.
56
4.8 (e) The relationship of color uniformity with days
to harvest for bunch 5.
57
4.8 (f) The relationship of color uniformity with days
to harvest for bunch 6.
58
4.8 (g) The relationship of color uniformity with days
to harvest for bunch 7.
59
4.8 (h) The relationship of color uniformity with days
to harvest for bunch 8.
60
4.8 (i) The relationship of color uniformity with days
to harvest for bunch 9.
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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