Variant-Order Statistics based Model for Real-Time Plant Species Recognition

Variant-Order Statistics based Model for Real-Time Plant Species Recognition

Автор: Heba F. Eid, Ashraf Darwish

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 9 Vol. 9, 2017 года.

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There are an urgent need of categorizing plant by its species, to help botanist setting up a plant species database. However, plant recognition model is still very challenging task in computer vision and can be onerous and time consuming because of inefficient representation approaches. This paper, proposes a recognition model for classifying botanical species from leaf images, using combination of variant-order statistics based measures. Hence, the spatial coordinates values of gray pixels defines the qualities of texture, for the proposed model a gray-scale approach is adopted for analyzing the local patterns of leaves images using second and higher order statistical measures. While, first order statistical measures are used to extract color descriptors from leaves images. Evaluation of the proposed model shows the importance of combining variant-order statistics measures for enhancing the plant leaf recognition accuracy. Several experiments on Flavia dataset and swedish dataset are conducted. Experimental results indicates that; the proposed model yields to improve the recognition rate up to 97.1% and 94.7% for both Flavia and Swedish dataset respectively; while taking less execution time.

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Plant Recognition, Leaf Descriptors Extraction, Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrices (GLRLM), leaf classification

Короткий адрес: https://sciup.org/15012684

IDR: 15012684

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