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Species Classification of Aquatic Plants using GRNN and ANFIS

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 47 - Number 4
Year of Publication: 2012
Authors:
S. Abirami
V. Ramalingam
S. Palanivel
10.5120/7180-9863

S Abirami, V Ramalingam and S Palanivel. Article: Species Classification of Aquatic Plants using GRNN and ANFIS. International Journal of Computer Applications 47(4):47-52, June 2012. Full text available. BibTeX

@article{key:article,
	author = {S. Abirami and V. Ramalingam and S. Palanivel},
	title = {Article: Species Classification of Aquatic Plants using GRNN and ANFIS},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {47},
	number = {4},
	pages = {47-52},
	month = {June},
	note = {Full text available}
}

Abstract

This work presents a method for plant species identification using the images of flowers. It focuses on the stable features of flowers such as color, texture and shape. K-means clustering is used to extract the color features. Texture segmentation is done using texture filters. Edge detectors are used to trace the boundary of the image and hence the shape features. Color, texture and shape features are extracted from 400 images of flowers. Classification of plants into dry land plants and aquatic plants, the aquatic plant species into wet and marsh aquatic plants, wet aquatic plants into iridaceae and epilobium family and marsh aquatic plants into malvaceae and onagraceae family, the iridaceae family into babiana and crocus species, the family epilobium into canum and hirsutum, the family malvaceae into mallow and pavonia, the family onagraceae into fuschia and ludwigia species are done using Generalized Regression Neural Network (GRNN) and Adaptive Neural Fuzzy Inference System (ANFIS) classifiers.

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