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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Size estimation and detection of disease for Betelvine leaf using image processing |
| Authors Name: | Aniruddha , Lakshmeesha Acharya , Aaron Sabastin , Akash Shetty , Vrunda Adkar |
| Unique Id: | IJSDR1805099 |
| Published In: | Volume 3 Issue 5, May-2018 |
| Abstract: | Plant species identification is a vital problem for biologists, environmentalists, agricultural researchers, taxonomists and in the field of Ayurvedic. Plant identification can be done by manually by the botanical experts using books or plant identification manual, but it can be time consuming and a low efficiency process. The proposed system brings out an efficient method for plant classification using color, texture and GLCM feature extraction with Support Vector Machine (SVM) is used as a classifier. The main phases of proposed approach are pre-processing Color recognition and classification, feature extraction and leaf classification. In the preprocessing stage, the acquired leaf image is resized and converted into binary image with filling the unwanted hole in order to extract the optimal feature. In the color recognition phase system classifies the leaf on the basis of various intensities like red, green to reduce the complexities. Feature extraction phase consists of geometrical feature and texture feature extraction which covers features like aspect ratio, rectangularity, convex area ratio, eccentricity, diameter, form factor, narrow factor, perimeter ratio, solidity, circularity, irregularity, contrast, homogeneity, correlation, energy, entropy. On the other end, training is given for leaves with the similar method and result is stored in the dataset. In the final phase SVM classifier is trained to identify the exact leaf disease. It is done to acquire high efficiency with less computational complexity. Training is carried out for 30 leaf images belong to 3 different classes. The proposed approach is more suitable for disease identification that have high accuracy with less computation time. |
| Keywords: | Leaf disease, Color, texture, GLCM, Support Vector Machine (SVM) |
| Cite Article: | "Size estimation and detection of disease for Betelvine leaf using image processing", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.3, Issue 5, page no.653 - 657, May-2018, Available :http://www.ijsdr.org/papers/IJSDR1805099.pdf |
| Downloads: | 000337062 |
| Publication Details: | Published Paper ID: IJSDR1805099 Registration ID:180303 Published In: Volume 3 Issue 5, May-2018 DOI (Digital Object Identifier): Page No: 653 - 657 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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