Image Pre-Processing Algorithm for Ficus deltoidea Jack (Moraceae) Varietal Recognition: A Repeated Perpendicular Line Scanning Approach

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Image pre-processing task is always the first crucial step in plant species recognition system which is responsible to keep precision of feature measurement process. Some of researchers have developed the image pre-processing algorithm to remove petiole section. However, the algorithm was developed using semi-automatic algorithm which is strongly believed to give an inaccurate feature measurement. In this paper, a new technique of automatic petiole section removal is proposed based on repeated perpendicular petiole length scanning concept. Four phases of petiole removal technique involved are: i) binary image enhancement, ii) boundary binary image contour tracing, iii) petiole section scanning, and iv) optimal image size retaining and cropping. The experiments are conducted using six varieties of Ficus deltoidea Jack (Moraceae) leaves. The experimental results indicate that the segmentation results are acceptably good since the digital leaf images have less than 1% of segmentation errors on several ground truth images.



  • Keywords

    Ficus deltoidea Jack; image pre-processing; image processing; leaf recognition; plant species recognition.

  • References

      [1] Corner EJH (1996), The complex of Ficus deltoidea; a recent invasion of the Sunda Shelf. Philosophical Transactions of the Royal Society of London 256, 281–317.

      [2] Nashriyah M, Nurrul Akmar R, Nor Zaimah AR, Norhaslinda H, Zanariah MN, Nur Fatihah HN, Abd Ghani Y & Abdul Manaf A (2012), Leaf Morphological Variations and Heterophyllyin Ficus deltoidea Jack (Moraceae). Sains Malaysiana 41, 5, 527–538.

      [3] Turner IM (1995), Catalogue of the vascular plants in Malaya. Garden’s Bulletin Singapore 47, 2, 347–757.

      [4] Norhaniza A, Sin CY, Chee ES, Nee KI & Renxin L (2007), Blood Glucose Lowering Effect of Ficus deltoidea Aqueous Extract. Malaysian Journal of Science 26, 1, 73–78.

      [5] Sulaiman MR, Hussain MK, Zakaria ZA, Somchit MN, Moin S, Mohamad AS & Israf DA (2008), Evaluation of the antinociceptive activity of Ficus deltoidea aqueous extract. Fitoterapia 79, 557–561.

      [6] Kumar N, Belhumeur PN, Biswas A, Jacobs DW, Kress WJ, Lopez I & Soares JVB, Leafsnape: A Computer Vision System for Automatic Plant Species Identification. 12th European Conference on Computer Vision, 2012, pp:502–516.

      [7] Satti V, Satya A & Sharma S (2013), An automatic leaf recognition system for plant identification using machine vision technology. International Journal of Engineering Science and Technology 5, 4, 874–879.

      [8] Jain AK, Duin PW & Mao J (2000), Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligent 22, 1, 4–37.

      [9] Polikar R, Pattern Recognition, John Wiley and Sons, New York, (2006), pp:1–22.

      [10] Chakrabarti S, Data Mining Know It All, Morgan Kaufmann Publishers, San Francisco, CA, (2009).

      [11] Kadir A, Nugroho LE, Susanto A & Santosa PI (2012), Performance Improvement of Leaf Identification System Using Principal Component Analysis. International Journal of Advanced Science and Technology 44, 113–124.

      [12] Asnor JI, Azura CS, Mohammad Hamiruce M, Shamsul K & Mohammad Ali JG, Automated Recognition of Ficus Deltoidea Using Ant Colony Optimization Technique. 8th IEEE Conference on Industrial Electronics and Applications, 2013, pp:296–300.

      [13] Lee CL & Chen SY (2006), Classification of Leaf Images. International Journal of Imaging Systems and Technology 16, 1, 15–23.

      [14] Du JX, Wang XF & Zhang GJ (2007), Leaf shape based species recognition. Applied Mathematics and Computation 185, 883–893.

      [15] Wu SG, Bao FS, Xu EU, Wang YX, Chang YF & Xiang QL, A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network. International Symposium on Signal Processing and Information Technology, 2007, pp:11–16.

      [16] Wang XF, Huang DS, Du JX, Xu H & Heutte L (2008), Classification of plant leaf images with complicated background. Applied Mathematics and Computation 205, 916–926.

      [17] Arora A, Gupta A, Bagmar N, Mishra S, Bhattacharya A, A Plant Identification System using Shape and Morphological Features on Segmented Leaflets: Team ITK, CLEF 2012. Online working notes, Evaluation Labs and Workshops, 2012.

      [18] McGuinness K & O'Connor NE (2010), A Comparative Evaluation of Interactive Segmentation Algorithms. Pattern Recognition 43, 2, 434–444.

      [19] Estrada FJ & Jepson AD, Quantitative Evaluation of a Novel Image Segmentation Algorithm. Computer Society Conference on Computer Vision and Pattern Recognition, 2005, pp:132–113.




Article ID: 11211
DOI: 10.14419/ijet.v7i2.15.11211

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.