Research Article Open Access

PLANT RECOGNITION USING STEREO LEAF IMAGE USING GRAY-LEVEL CO-OCCURRENCE MATRIX

Hermawan Syahputra1, Agus Harjoko2, Retantyo Wardoyo2 and Reza Pulungan2
  • 1 State University of Medan, Indonesia
  • 2 Universitas Gadjah Mada, Indonesia
Journal of Computer Science
Volume 10 No. 4, 2014, 697-704

DOI: https://doi.org/10.3844/jcssp.2014.697.704

Submitted On: 29 October 2013 Published On: 24 December 2013

How to Cite: Syahputra, H., Harjoko, A., Wardoyo, R. & Pulungan, R. (2014). PLANT RECOGNITION USING STEREO LEAF IMAGE USING GRAY-LEVEL CO-OCCURRENCE MATRIX. Journal of Computer Science, 10(4), 697-704. https://doi.org/10.3844/jcssp.2014.697.704

Abstract

Adequate knowledge, such as information about the unique characteristics of each plant, is necessary to identify plant. Researchers have made plant recognition based on leaf characteristics. The leaf image-based plant recognition in view of different angles is a new challenge. In this study, the research on the plant recognition was conducted based on leaf images resulted from 3D stereo camera. The 3D images are very influential in the development of computer vision theory, which can provide more detailed information of an object. One of the information that can be obtained is about the position of the object in its image with the background as well as of the camera. One of the ways used to obtain such information is to calculate the disparity. However, this method will only tell the position of the object compared to other objects without that of range. Sum Absolute Different (SAD) is a method that can be used to find the disparity value. The SAD method does not require heavy computations and long process. Before calculating the disparity, all the images should be previously segmented. The objective of this segmentation is to separate all the objects from the background. Furthermore, filtering and polynomial transformation at the results of disparity is necessary to improve the quality of resultant images. Furthermore, 22 features were extracted using GLCM features (second order statistics) of images resulted from disparity improvement. The highest accuracy of match in the recognition of plant varieties was obtained at 50 cm distance and in the recognition of three plant varieties was 83.3%.

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Keywords

  • Disparity
  • Plant Recognition
  • Stereo Vision
  • GLCM