Ripeness detection of oil palm fresh fruit bunches using fluorescence sensor

Classification of oil palm fresh fruit bunches (FFB) into its correct ripeness category (under-ripe, ripe and over-ripe) is a critical factor that dictates efficient oil palm milling operations. This study investigates the fluorescence sensor to determine which excitation LEDs are suitable in discr...

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Main Author:	Mohd Hazir, Mohd Hafiz
Format:	Thesis
Language:	English
Published:	2011
Subjects:	Oil palm - Malaysia Luminescene
Online Access:	http://psasir.upm.edu.my/id/eprint/41818/1/FK%202011%20151R.pdf
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spelling	my-upm-ir.418182016-03-03T01:19:07Z Ripeness detection of oil palm fresh fruit bunches using fluorescence sensor 2011-11 Mohd Hazir, Mohd Hafiz Classification of oil palm fresh fruit bunches (FFB) into its correct ripeness category (under-ripe, ripe and over-ripe) is a critical factor that dictates efficient oil palm milling operations. This study investigates the fluorescence sensor to determine which excitation LEDs are suitable in discriminating between the different ripeness categories. To determine this, we used a Multiplex®3 sensor, which has an active fluorescence sensor system; comprising of 9 excitation LED (6 UV and 3 RGB for red, green and blue excitations) and 3 photodiodes for the emitted fluorescence in the yellow (590 nm),red (685 nm) and far red ranges (735 nm), to detect oil palm FFB ripeness categories. The in-field signal value data were collected using the sensor system from a total of one hundred and eighty (180) oil palm FFB. These oil palms FFB were classified into underripe,ripe, and over-ripe ripeness categories. Feature selection method, the rank method based on chi-square value was used to select the best predictors among available features. Fourteen classification methods (SPSS Classification TreeTM (QUEST, CHAID, and CRT), SPSS Discriminant Analysis (Enter independent together), STATISTICA Stochastic Gradient Boosting Trees, STATISTICA Interactive Tree (C&RT), STATISTICA MARSplines, STATISTICA General Stepwise Linear Discriminant Analysis, STATISTICA Automated Neural Networks Classification, STATISTICA Random Forest For Classification, Machine Learning (Support Vectors Machine, Naïve Bayes Classifier and k-Nearest Neighbour)),were used to assess the applicability of using the sensor system. Based on the classification accuracies, data analysis on the predictors indicated that the signal values of the data could be valuable in predicting the maturity stage of the oil palm FFB. The STATISTICA Stochastic Gradient Boosting Trees yielded highest average overall accuracies of 89.4% for the correct classification of oil palm FFB using the blue to red fluorescence ratio (BRR_FRF) as a predictor. Additionally, the average individual classes (under-ripe, ripe and over-ripe) classification accuracies were also higher than 76%. Thus, fluorescence sensing using the blue to red fluorescence ratio (BRR_FRF) as a predictor is useful for oil palm FFB ripeness detection under field conditions. This research will be useful for future development of low cost non-destructive, automatic and real time oil palm FFB grading system. Oil palm - Malaysia Luminescene 2011-11 Thesis http://psasir.upm.edu.my/id/eprint/41818/ http://psasir.upm.edu.my/id/eprint/41818/1/FK%202011%20151R.pdf application/pdf en public masters Universiti Putra Malaysia Oil palm - Malaysia Luminescene
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Oil palm - Malaysia Luminescene
spellingShingle	Oil palm - Malaysia Luminescene Mohd Hazir, Mohd Hafiz Ripeness detection of oil palm fresh fruit bunches using fluorescence sensor
description	Classification of oil palm fresh fruit bunches (FFB) into its correct ripeness category (under-ripe, ripe and over-ripe) is a critical factor that dictates efficient oil palm milling operations. This study investigates the fluorescence sensor to determine which excitation LEDs are suitable in discriminating between the different ripeness categories. To determine this, we used a Multiplex®3 sensor, which has an active fluorescence sensor system; comprising of 9 excitation LED (6 UV and 3 RGB for red, green and blue excitations) and 3 photodiodes for the emitted fluorescence in the yellow (590 nm),red (685 nm) and far red ranges (735 nm), to detect oil palm FFB ripeness categories. The in-field signal value data were collected using the sensor system from a total of one hundred and eighty (180) oil palm FFB. These oil palms FFB were classified into underripe,ripe, and over-ripe ripeness categories. Feature selection method, the rank method based on chi-square value was used to select the best predictors among available features. Fourteen classification methods (SPSS Classification TreeTM (QUEST, CHAID, and CRT), SPSS Discriminant Analysis (Enter independent together), STATISTICA Stochastic Gradient Boosting Trees, STATISTICA Interactive Tree (C&RT), STATISTICA MARSplines, STATISTICA General Stepwise Linear Discriminant Analysis, STATISTICA Automated Neural Networks Classification, STATISTICA Random Forest For Classification, Machine Learning (Support Vectors Machine, Naïve Bayes Classifier and k-Nearest Neighbour)),were used to assess the applicability of using the sensor system. Based on the classification accuracies, data analysis on the predictors indicated that the signal values of the data could be valuable in predicting the maturity stage of the oil palm FFB. The STATISTICA Stochastic Gradient Boosting Trees yielded highest average overall accuracies of 89.4% for the correct classification of oil palm FFB using the blue to red fluorescence ratio (BRR_FRF) as a predictor. Additionally, the average individual classes (under-ripe, ripe and over-ripe) classification accuracies were also higher than 76%. Thus, fluorescence sensing using the blue to red fluorescence ratio (BRR_FRF) as a predictor is useful for oil palm FFB ripeness detection under field conditions. This research will be useful for future development of low cost non-destructive, automatic and real time oil palm FFB grading system.
format	Thesis
qualification_level	Master's degree
author	Mohd Hazir, Mohd Hafiz
author_facet	Mohd Hazir, Mohd Hafiz
author_sort	Mohd Hazir, Mohd Hafiz
title	Ripeness detection of oil palm fresh fruit bunches using fluorescence sensor
title_short	Ripeness detection of oil palm fresh fruit bunches using fluorescence sensor
title_full	Ripeness detection of oil palm fresh fruit bunches using fluorescence sensor
title_fullStr	Ripeness detection of oil palm fresh fruit bunches using fluorescence sensor
title_full_unstemmed	Ripeness detection of oil palm fresh fruit bunches using fluorescence sensor
title_sort	ripeness detection of oil palm fresh fruit bunches using fluorescence sensor
granting_institution	Universiti Putra Malaysia
publishDate	2011
url	http://psasir.upm.edu.my/id/eprint/41818/1/FK%202011%20151R.pdf
_version_	1747811890529042432

Ripeness detection of oil palm fresh fruit bunches using fluorescence sensor

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