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Colour Feature Extraction Techniques for Real Time System of Oil Palm Fresh Fruit Bunch Maturity Grading

IOP conference series. Earth and environmental science, 2020-07, Vol.540 (1), p.12092 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1755-1307 ;EISSN: 1755-1315 ;DOI: 10.1088/1755-1315/540/1/012092

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  • Title:
    Colour Feature Extraction Techniques for Real Time System of Oil Palm Fresh Fruit Bunch Maturity Grading
  • Author: Alfatni, Meftah Salem M ; Mohamed Shariff, Abdul Rashid ; Ben Saaed, Osama M. ; Albhbah, Atia Mahmod ; Mustapha, Aouache
  • Subjects: Algorithms ; Artificial neural networks ; Classification ; Classifiers ; Color ; Farms ; Feature extraction ; Fruits ; Histograms ; Human error ; Image acquisition ; Image classification ; Image processing ; Image segmentation ; Inspection ; Learning algorithms ; Learning theory ; Machine learning ; Maturity ; Neural networks ; Palm oil ; Performance evaluation ; Quality ; Real time ; Segmentation ; Vegetable oils
  • Is Part Of: IOP conference series. Earth and environmental science, 2020-07, Vol.540 (1), p.12092
  • Description: According to the natural ripeness phenomena of oil palm fruit, the oil palm fresh fruit bunch (FFB) ripeness is divided into three different categories called, under ripe, ripe, and overripe. The current method in the mills for oil palm FFB grading is manually using human graders. However, the manual grading method is subjective and prone to human errors. Thus, accurate classification of oil palm FFB with fast and easy process is necessary in oil palm farms and in the palm oil industry to grade high-quality products, especially when classifying a large amount of fruits. In this paper, an image processing algorithm scenario represented by image acquisition, segmentation, and feature extraction is implemented in real time system for oil palm FFB maturity grading. This study presents an automated inspection for the oil palm FFB using an external grading system processing based on the colour feature extraction technologies; namely, color histogram and statistical color feature (mean and standard deviation), were used to extract the oil palm FFB image's color features. An artificial neural network (ANN) classifier as supervised machine learning technique is applied to train and test the system performance accuracy based on area under curve (AUC) of the receiver operation characteristic (ROC) as classifier performance evaluation. The result show that the colour histogram technique based on the ANN classifier was observed to be a more robust and accurate classifier, with overall 94% correct ripeness classification accuracy, for oil palm FFB maturity grading compared to the other techniques applied and tested in this study.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1755-1307
    EISSN: 1755-1315
    DOI: 10.1088/1755-1315/540/1/012092
  • Source: IOPscience (Open Access)
    IOP 英国物理学会OA刊
    Alma/SFX Local Collection
    ProQuest Central
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