An Efficient Classification of Leaves and Based On Leaf Architecture Using MLPNN Algorithm
- D.Keerthika
- K.Nandhinipriya
- S.Sivaranjani
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Abstract— Plant classification has a broad application prospective in agriculture and medicine, and is especially significant to the biology diversity research. As plants are vitally important for environmental protection, it is more important to identify and classify them accurately. Plant leaf classification is a technique where leaf is classified based on its different morphological features. Most of the existing system introduced a various method for classification. But it does not achieve satisfactory result.
To solve this problem the proposed system introduced a firefly algorithm based biological species classification. This research focuses on using digital image processing for the purpose of automate classification and recognition of plants based on the images of the leaves. The system consists of 5 main modules, 1) image acquisition, 2) image preprocessing, 3) image feature extraction 4) feature selection and 5) classification. In the image acquisition module leaf image is captured by using digital camera. In the image preprocessing module, various image processing techniques are applied for preparing a leaf image for the features extraction process. Then texture, shape, Eccentricity and leaf perimeter are extracted from enhanced image. Then optimal features are extracted by using firefly algorithm. In the image recognition, the leaf images are classified by using single MLPNN with Levenberg-Marquardt back-propagation algorithm. In the display result module displays the recognition results. The experimental results show that the proposed system achieves higher performance compared with the existing system in terms of accuracy, precision and recall.
D.Keerthika, K.Nandhinipriya, S.Sivaranjani. "An Efficient Classification of Leaves and Based On Leaf Architecture Using MLPNN Algorithm".INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH ISSN:2321-9939, Vol.6, Issue 1, pp.305-310, URL :https://rjwave.org/ijedr/papers/IJEDR1801051.pdf
Volume 6 Issue 1
Pages. 305-310
