Adaptive region growing for automated oil palm fruit quality recognition

LaylaWantgli Shrif Amosh, Siti Norul Huda Sheikh Abdullah, Che Radziah Che Mohd. Zain, Jinjuli Jameson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages184-192
Number of pages9
Volume8237 LNCS
DOIs
Publication statusPublished - 2013
Event3rd International Visual Informatics Conference, IVIC 2013 - Selangor
Duration: 13 Nov 201315 Nov 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8237 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Visual Informatics Conference, IVIC 2013
CitySelangor
Period13/11/1315/11/13

Fingerprint

Region Growing
Palm oil
Fruit
Fruits
Seed
Image segmentation
Color Image Segmentation
Color
Malaysia
K-means Clustering
Grading
Rubber
Harvesting
Clustering Methods
Image Segmentation
Crops
Computational Cost
High Accuracy
Costs

Keywords

  • automated visual inspection
  • color image segmentation
  • seed region growing

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Amosh, L. S., Sheikh Abdullah, S. N. H., Che Mohd. Zain, C. R., & Jameson, J. (2013). Adaptive region growing for automated oil palm fruit quality recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8237 LNCS, pp. 184-192). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8237 LNCS). https://doi.org/10.1007/978-3-319-02958-0_18

Adaptive region growing for automated oil palm fruit quality recognition. / Amosh, LaylaWantgli Shrif; Sheikh Abdullah, Siti Norul Huda; Che Mohd. Zain, Che Radziah; Jameson, Jinjuli.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8237 LNCS 2013. p. 184-192 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8237 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Amosh, LS, Sheikh Abdullah, SNH, Che Mohd. Zain, CR & Jameson, J 2013, Adaptive region growing for automated oil palm fruit quality recognition. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 8237 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8237 LNCS, pp. 184-192, 3rd International Visual Informatics Conference, IVIC 2013, Selangor, 13/11/13. https://doi.org/10.1007/978-3-319-02958-0_18
Amosh LS, Sheikh Abdullah SNH, Che Mohd. Zain CR, Jameson J. Adaptive region growing for automated oil palm fruit quality recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8237 LNCS. 2013. p. 184-192. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-02958-0_18
Amosh, LaylaWantgli Shrif ; Sheikh Abdullah, Siti Norul Huda ; Che Mohd. Zain, Che Radziah ; Jameson, Jinjuli. / Adaptive region growing for automated oil palm fruit quality recognition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8237 LNCS 2013. pp. 184-192 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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