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Plant Leaf Recognition Using Machine Learning Techniques

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • R. Sujee

    (Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Department of Computer Science and Engineering)

  • Senthil Kumar Thangavel

    (Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Department of Computer Science and Engineering)

Abstract

Leaves can be of more importance in the context of recognition. Creating a model will help in recognizing them for different applications like Medicine and Herbal analysis. The leaves has features that can be statistical based or at high level. It can include edge and any features built over pixel level. Edge identification is used for data extraction, image segmentation and data compression. In this paper Statistical features for set of leaves are identified, Leaves are classified using multi class SVM and Edges of a leaf is identified by using canny, prewitt and sobel edge recognition techniques base on various Gaussian mask. Convolution Neural Network is used to classify the given image under 14 categories. From the trial results, it is seen that canny edge identification method gives preferable outcomes over prewitt and sobel edge recognition strategies. The paper also provides directions for using Convolution Neural Network for Leaf recognition.

Suggested Citation

  • R. Sujee & Senthil Kumar Thangavel, 2020. "Plant Leaf Recognition Using Machine Learning Techniques," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1433-1444, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_147
    DOI: 10.1007/978-3-030-41862-5_147
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