Scale invariant feature transform technique for weed classification in oil palm plantation

Kamarul Hawari Ghazali, Mohd. Marzuki Mustafa, Aini Hussain, Saifudin Razali

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

This study presents a new and robust technique using Scale Invariant Feature Transform (SIFT) for weed classification in oil palm plantation. The proposed SIFT classification technique was developed to overcome problem in real application of image processing such as varies of lightning densities, resolution and target range which contributed to classification accuracy. In this study, SIFT classification algorithm is used to extract a set of feature vectors to represent the input image. The set of feature vectors then can be used to classify weed. In general, the weeds can be classified as either broad or narrow. Based on this classification, a decision will be made to control the strategy of weed infestation in oil palm plantations. The effectiveness of the robust SIFT technique has been tested offline where the input images were captured under varies conditions usch as different lighting effects, ambiguity resolution values, variable range of object and many sizes of weed which simulate the actual field conditions. The proposed SIFT resulted in over 95.7% accuracy of classification of weed in palm oil plantation.

Original languageEnglish
Pages (from-to)1179-1187
Number of pages9
JournalJournal of Applied Sciences
Volume8
Issue number7
Publication statusPublished - 2008

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Palm oil
Mathematical transformations
Lightning
Image processing
Lighting

Keywords

  • Gaussian
  • Key descriptor
  • SIFT
  • Weed

ASJC Scopus subject areas

  • General

Cite this

Scale invariant feature transform technique for weed classification in oil palm plantation. / Ghazali, Kamarul Hawari; Mustafa, Mohd. Marzuki; Hussain, Aini; Razali, Saifudin.

In: Journal of Applied Sciences, Vol. 8, No. 7, 2008, p. 1179-1187.

Research output: Contribution to journalArticle

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