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Adaptive Region Growing for Automated Oil Palm Fruit Quality Recognition

  • LaylaWantgli Shrif Amosh
  • Siti Norul Huda Sheikh Abdullah
  • Che Radiziah Che Mohd
  • Jinjuli Jameson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8237)

Abstract

Besides rubber and rice, oil palm or Elaeis Guineensis remains as one of the most important plantation crops in Malaysia. Unfortunately, the lack of experience in oil palm fruit grading among the plucking farmers results in wrong estimation when harvesting. This affects production, negatively. Meanwhile, region growing conventional image segmentation techniques need manually or fixed initial seed selection which, actually, increases the computational cost, as well as, implementation time. Hence, the main goal of this study is to improve the seed region growing algorithm in order to gain higher accuracy in segmenting color information for oil palm fruit image. This study presents n-Seed Region Growing (n-SRG) for color image segmentation by choosing adaptive numbers of seed. The data sample consists of 80 images which comprises and two ripeness classes (ripe and unripe).The proposed work has out-performed the k-mean clustering method with 86% and 80% of average accuracy rates correspondingly.

Keywords

color image segmentation seed region growing automated visual inspection 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • LaylaWantgli Shrif Amosh
    • 1
  • Siti Norul Huda Sheikh Abdullah
    • 1
  • Che Radiziah Che Mohd
    • 2
  • Jinjuli Jameson
    • 1
  1. 1.Pattern Recognition Research Group, Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
  2. 2.School of Bioscience and Biotechnology, Faculty of Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia

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