The week's pick
Reseach Article
A Novel Approach for Plant Identification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 177 - Number 5 |
| Year of Publication: 2017 |
| Authors: Sonali A. Kanade, Chaitali S. Patil, Tejaswini S. Pawar, Ashok R. Ambore, Hemant D. Sonawavne |
10.5120/ijca2017915752
|
Sonali A. Kanade, Chaitali S. Patil, Tejaswini S. Pawar, Ashok R. Ambore, Hemant D. Sonawavne . A Novel Approach for Plant Identification. International Journal of Computer Applications. 177, 5 ( Nov 2017), 7-9. DOI=10.5120/ijca2017915752
Abstract
Plant Identification has a place with a particular application space of data mining. Pictures of plant leaves are generally utilized as the primary component to recognize a plant from another. For legitimate recognizable proof, highlight extraction is essential. In the literature, most plant acknowledgment frameworks utilize the highlights alongside a grouping technique, which has been adjusted or changed to confront this kind of application. In this paper, we are building up a portable application for distinguishing restorative plants which utilizes Profound Learning and Convolutional Neural System to take in discriminative highlights from leaf pictures with classifiers for plant ID. We propose three new geometric highlights that depict the vertical and level symmetry of clears out. These highlights are easy to separate from pictures. As per the consequences of trials, when these highlights are utilized as a part of conjunction with other surely understood geometric attributes, the execution of traditional order strategies is amazingly moved forward. To demonstrate the adequacy of the proposition, We test few classifiers with pictures of leaves openly accessible on We, This framework will manage professionals of Ayurveda and ordinary android clients too.
References
- G. Cerutti, L. Tougne, D. Coquin, and A. Vacavant, “Leaf margins as sequences: A structural approach to leaf identification,” Pattern Recognition Letters, vol. 49, pp. 177 – 184, 2014. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0167865514002335
- J. S. Cope, D. Corney, J. Y. Clark, P. Remagnino, and P. Wilkin, “Plant species identification using digital morphometrics: A review,” Expert Systems with Applications, vol. 39, no. 8, pp. 7562 – 7573, 2012. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0957417412000863
- C.-Y. Gwo, C.-H.Wei, and Y. Li, “Rotary matching of edge features for leaf recognition,” Computers and Electronics in Agriculture, vol. 91, pp. 124 – 134, 2013.[Online]. Available: http://www.sciencedirect.com/science/article/pii/S0168169912002906
- C. Zhao, S. S. Chan, W.-K. Cham, and L. Chu, “Plant identification using leaf shapes-a pattern counting approach,” Pattern Recognition, vol. 48, no. 10, pp. 3203 – 3215, 2015, discriminative Feature Learning from Big Data for Visual Recognition. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0031320315001284
- S. Watcharabutsarakham, W. Sinthupinyo, and K. Kiratiratanapruk, “Leaf classification using structure features and support vector machines,”in Information Science and Service Science and Data Mining(ISSDM), 2012 6th International Conference on New Trends in. IEEE, 2012.
- J. I. Arribas, G. V. S´anchez-Ferrero, G. Ruiz-Ruiz, and J. G´omez-Gil, “Leaf classification in sunflower crops by computer vision and neural networks,” Computers and Electronics in Agriculture,vol. 78, no. 1, pp.9–18,2011. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0168169911001220
- T. Munisami, M. Ramsurn, S. Kishnah, and S. Pudaruth, “Plant leaf recognition using shape features and colour histogram with k-nearest neighbor classifiers,” Procedia Computer Science, vol. 58, pp. 740 –747, 2015, second International Symposium on Computer Vision and the Internet (VisionNet15). [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1877050915022061.
- Uluturk and A. Ugur, “Recognition of leaves based on morphological features derived from two half-regions,” in Innovations in Intelligent Systems and Applications (INISTA), 2012 International Symposium on, July 2012, pp. 1–4.
Index Terms
Keywords