Oil Palm Ripeness Detector (OPRID) and Non-Destructive ...

FACULTY OF ENGINEERING

Oil Palm Ripeness Detector (OPRID) and

Non-Destructive Thermal Method of

Palm Oil Quality Estimation

Abdul Rashid Mohamed Shariff, Shahrzad Zolfagharnassab, Alhadi Aiad H. Ben

Dayaf, Goh Jia Quan, Adel Tursun,, Assoc. Prof. Rimfiel Janius, Assoc Prof.

Dr.Hawa ZE Jaafar, Prof.Dr.Aris Ishak, Prof.Dr.Reza Ehsani


INTRODUCTION

• Oil palm (Elaeis guineensis) was first introduced to Malaysia as an ornamental plant in 1870.

Since 1960, planted area had increased at a rapid pace. In 1985, 1.5 million hectares were

planted with palm tree, and it had increased to 4.3 million hectares in 2007. It has become the

most important commodity crop in Malaysia. As of 2011, the total planted area was 4.917

million hectares.(MPOB 2011)

http://www.etawau.com/OilPalm/Fruit6Month.htm

http://www.etawau.com/OilPalm/Fruit6Month.htm


INTRODUCTION

Oil palm is the most productive

vegetable oil crop, capable of

producing 4.27 t of palm oil per

hectare per year.(MPOB 2011)

Malaysia with producing up to 18,400

thousand metric tons is the second

largest producer of palm oil and %86

of worldwide export.(USDA, 2011).


First (1st) Part Of The Presentation:

FFBs maturity classification and oil analysis

correlation


INTRODUCTION

.

• Grading of oil palm fruits is conventionally observed by human vision. This

is used for ripeness classification.

• Today many types of research has been carried out to find the correlation

between oil content and quality of the oil palm fruit based on its colour

(RGB).

• These researches followed more advanced methods and techniques by using

different types of sensing techniques. Accordingly some near sensing

devices has been constructed in order to achieve real time classification

result of certain fruit.

• Oil Palm Fresh Fruit Bunches Ripeness Detector (OPRID) using multi

spectral bands has been designed by integrated different sensors and sources

of illumination.


Problem Statement

• A fast, accurate, and objective ripeness classification of oil palm fruit

bunches for unripe, under-ripe, ripe, and over-ripe grading in real time has

its limitations using traditional methods.

• Traditional methods of oil palm FFB quality assessment are costly and

tedious.

• There is a need not only for automatic detection of ripeness but also

automatic determination of oil content and oil quality parameters


Objectives

The main objectives of this work are:

• To determine maturity classification of FFB using OPRID.

• To determine correlation between OPRID signals and oil palm

parameters.


Technical Information Of OPRID


OPRID preparation steps.

• OPRID calibration

• Sensors calibration.Sensors require calibration to quantify the sensor’s response to known radiometric input and to

characterize the interactions and dependencies between the optical, mechanical, and electronic

components

• Mode 1

White balance calibration: using pure and smooth white surface with area that fit

OPRID slot, then record all AU readings using four detectors with ten LEDs separately

(40 models).

• Mode 2

Black level calibration: using same procedure in mode1 but change white surface with

black one.


OPRID preparation steps.

• LEDs Adjusting

• For using the device as efficiently as possible, where OPRID sensors have high spectral resolution

(AU=65280) and LEDs power can be adjusted in many levels, so LEDs should be adjusted to achieve

highest performance of device, and this will be done but with two consideration:

• First, avoid reaching saturation point, where saturation means that the use of this reading cannot be

relied upon in the classification tasks and comparison between certain values.

• Secondly, the LED power should be stronger enough to extent AUs range that makes the task of

distinguishing between readings is more effective.


Research flowchart

1. Data collection 2. Data preprocessing

3. Data analysis


Methodology


Methodology

Eliminated


Methodology


Results (1st experiment of FFB)


Results (2nd experiment of FFB)


Results

98,5

100

98,5 98,5 98,5 98,5 98,5

100

98,5

100 100

95,5

97

95,5 95,5

97 97

95,5

98,5

95,5

97 97

93

94

95

96

97

98

99

100

101

Green S1 Red S2 DRedS2 FRedS3 BlueS4 GreenS4 AmberS4 RedS4 DRedS4 FRedS4 IRedS4

FFB Experiment Classification Accuracy

1st exp.

2nd exp.


Highest

accuracy

Models Algorithms

First experiment 100% RedS2,

RedS4,

FRedS4,

IRedS4

Logistic, Simple

Logistic, LMT,

Second

experiment

98.5% RedS4 Logistic

Results


• Able to determine ripeness of FFB by classifying them into

four categories of unripe, under ripe, ripe and over ripe.

• Able to check classification accuracy of FFB by achieving

accuracy of 100%.

• Also able to determine the best algorithms for classification of

FFB which is the LMT and RandomForest.

Summary of Results


Oil Analysis


Oil Analysis (2)


Correlation between OPRID and laboratory classification


Second (2nd) Part Of The Presentation

Oil Palm quality parameter estimation based on

thermal imaging technique


INTRODUCTION• Temperature measurement is an important aspect in any industrial

process and infrared thermography has revolutionized the concept of temperature measurement ( R.Vadivambal, 2010).

• Temperature measurements mostly performed using some contact instrument like thermometers, thermocouples, thermistors, and resistance temperature detectors.

• While infrared thermal imaging is a non-contact, nondestructivetechnique which provides temperature mapping of the material(R.Vadivambal, 2010). Therefore, use of infrared thermal imaging iswidely increasing in many fields.


Problem statement• The maturity or ripeness of the oil palm fruits influences the quality of oil

palm.

• Conventional method includes manual detection of FFB ripeness by

counting the number of loosened fruits per bunch.

• This manual sorting of oil palm FFB is a time-consuming, costly,

needs many workers and the results may have the human error.

• Currently, grading of palm oil fruit is performed through visual

inspection using the surface color as the main quality attribute.

• Color is the most important indication of FFB ripeness but there is no

inspection of the relationship between color and optimum FFB oil

content, FFA, PV, DOBI and CAROTIN


Objectives

This research investigates the potential of infrared images (Thermal images) as

a predictor to estimate the oil content, Free Fatty Acid (FFA), peroxide value

(PV), Deterioration of Bleachability Index (DOBI) and Carotene.

.

The research objective are:

• To investigate the correlation of the thermal image oil content, Free Fatty

Acid (FFA), peroxide value (PV), Deterioration of Bleachability Index (DOBI)

and Carotene.

• To develope a technique to predict the total pecentage of oil content and

Free Fatty Acid (FFA), peroxide value (PV), Deterioration of Bleachability

Index (DOBI) and Carotene in the fresh fruit bunch.


METHODOLOGY

• Obtaining raw data from the thermal camera is a temperature array of the thermal distribution of the object’s surface.

• Transforming this temperature array into an image format to create a thermal image and extract the relevant feature polygon of FFB by using thermal image processing software “FLIR reporter wizard” and “thermaCAM researcher pro 2.10”

• Determine the relationship between oil content, Free Fatty Acid (FFA), peroxidevalue (PV), Deterioration of Bleachability Index (DOBI) and Carotene in the FFB andtemperature data from the thermal images by using the correlation and regressiontechniques.

• Development a predictive model using Artificial Neural network (ANN) to estimate the total percentage of oil content, FFA and other quality parameters


First Data collection• Fresh fruit bunches of varying ripeness based on

the number of loosened fruit and visualobservation of fruit color were harvested fromUnited Plantation Research and Development(UPRD) Center in Teluk Intan , Perak Malaysia.

• 135 harvested bunches were weightedand thermal images from three sides ofthe fresh fruit bunches were thencaptured with a FLIR E60 camera

• The oil palm bunches were from the Nigresenscultivar according to three maturity categories:Under Ripe, Ripe and Over Ripe.

• For each category, the images were collected based on three types of weight:0-15 kg, 15-25kg and above 25kg.


• After finishing the capturing session

for the FFB image, the bunches will

be send to laboratory for chemical

analysis to determine its oil content ,

FFA, PV, DOBI and carotene.


First Data Analysis


Second Data Collection

• The data were collected with two thermal

camera E60 and T440.

• The two set of data from three sides of 135 FFB were

collected in United Plantation Research and

Development in Teluk Intan(UPRD), Perak.

• The oil palm bunches same as first data were from the Nigresens cultivar according to three maturity categories: Under Ripe, Ripe and Over Ripe.


Image processing

a:Under Ripe FFB

b: Ripe FFB

c: Over Ripe FFB


Normality and Homogeneity Test


Temperature and FFA

• Temperature = Palm temperature – Atmosphere

temperature


Temperature and Oil Content


Temperature and PV

0,370,32

0,27

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

1 2 3


Temperature and DOBI

4,34,2

3,6

3,2

3,4

3,6

3,8

4

4,2

4,4

1 2 3


Temperature and Oil Content 2

25,07

26,62

26,84

24

24,5

25

25,5

26

26,5

27

UR R OR


Temperature and FFA 2


Correlation coefficient

• P= 0.000 and r= -.776

• There is a strong relationship between temperature and FFA


Temperature and DOBI 2


Temperature and CAROTINE 2


Oil content prediction By ANN

Each group has 90 samples


Principal Component analysis


PCA: Extraction Method


Scatterplot Matrix of Component Scores


Oil content prediction By ANN with reduce feature

based on PCA


FFA prediction by PCA-ANN



PV prediction by PCA-ANN



DOBI prediction by PCA-ANN


Summary of Results

• The performance of the ANN for Oil content, FFA and other quality parameters

prediction of oil palm FFB was investigated using two methods: training ANN

with full features and training ANN with reduced features based on the

Principal Component Analysis (PCA) data reduction technique.

• PCA-ANN predictor can be good indicator to predict oil content and oil quality

parameters:

• Oil content 94.6%

• FFA 77%

• PV 83.3%

• DOBI 75.9%


Conclusion

• This technique offers a non-destructive means of

assessing palm oil quality and can enable oil yield and/or

oil quality (FFA) determination.

• This study allows for rapid screening of FFB ripeness and

relates it to oil content in the FFB and accordingly the

amount of oil that can be extracted from a consignment of

FFB arriving at the mill


GRACIAS

THANK YOU

TERIMA-KASIH

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