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:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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ISSN 2228-9860
eISSN 1906-9642
CODEN: ITJEA8


FEATURE PEER-REVIEWED ARTICLE

Vol.13(7)(2022)

  • Enhanced Medical Plant Leaf Edge Detection Method using Non-Linear Constrained Optimization

    P.Loganathan, R.Karthikeyan (Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, INDIA).

    Disciplinary: Computer Engineering, Digital Image Processing.

    ➤ FullText

    doi: 10.14456/ITJEMAST.2022.127

    Keywords: Medical plant leaf; Edge detection; constrained optimization; Penalty method; ROC curve analysis.

    Abstract
    The accuracy of the higher level of image processing depends primarily on edge detection which is a lower level of image processing task. The accuracy of medical plant leaf edge detection determines the success of the applications developed based on computer vision and machine vision for object recognition and scene interpretation from an image. It is essential to have an effective and definite edge detection method with accurate edge information. This research paper proposes to identify the edges using constrained optimization on a medical plant leaf. The penalty method is a nonlinearly constrained optimization technique used for solving both equality and inequality constraints. It was used to solve the constrained problem by converting it into an unconstrained problem using the penalty function. Nelder mead algorithm which is a derivative-free unconstrained optimization method was used to solve the unconstrained problem to obtain optimal edge regions from an image. In this paper Receiving Operating Characteristics (ROC) curve analysis was used for the performance analysis to justify the proposed method's accuracy.

    Paper ID: 13A7A

    Cite this article:

    Loganathan, P., Karthikeyan, R. (2022). Enhanced Medical Plant Leaf Edge Detection Method using Non-Linear Constrained Optimization. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 13(7), 13A7A, 1-8. http://TUENGR.COM/V13/13A7A.pdf DOI: 10.14456/ITJEMAST.2022.127

References

  1. Francis et al., "Disease Detection and Classification in Agricultural Plants Using Convolutional Neural Networks - A Visual Understanding", International Conference on Signal Processing and Integrated Networks, 2019, pp. 1063-1068.
  2. Lee et al., "How deep learning extracts and learns leaf features for plant classification", Pattern Recognition 71, 2017, pp. 1-13.
  3. Shen et al., "Msr-net: Low-light image enhancement using deep convolutional network. arXiv preprint arXiv:1711.02488. 2017.
  4. Mohanty et al., "Using deep learning for image based plant disease detection", Frontiers in plant science, 2016.
  5. Grinblat, Guillermo et al., "Deep learning for plant identification using vein morphological patterns", Computers and Electronics in Agriculture 127, 2016, pp. 418-424.
  6. Larese, Monica et al., "Automatic classification of legumes using leaf vein image features", Pattern Recognition 47, no. 1, 2014, pp. 158-168.
  7. Ravindra Naik et al., "Plant leaf and disease detection by using HSV features and SVM classifier", Int. J. Eng. Sci. Comput, 2016, pp. 3794-3797.
  8. Q-K Man et al., "Recognition of Plant Leaves Using Support Vector Machine", International Conference on Intelligent Computing, 2008, pp. 192-199.
  9. Trichili et al., "A survey and evaluation of edge detection operators application to medical images", IEEE International Conference on Systems, Man and Cybernetics, vol. 4, 2002.
  10. D.Marr and E.Hildreth, "Theory of Edge Detection", Proceedings of the Royal Society of London,Series B,Biological Sciences, vol.207, no.1167, pp.187-217.
  11. J. Canny, "A Computational Approach to Edge Detection", IEEE Transactions on Pattern Analysis And Machine Intelligence, vol. PAMI- 8, no.6, November 1986


Other issues:
Vol.13(6)(2022)
Vol.13(5)(2022)
Vol.13(4)(2022)
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