Vol.62, No.3, 2020, pp.1445-1472, doi:10.32604/cmc.2020.08857
OPEN ACCESS
RESEARCH ARTICLE
CNN Approaches for Classification of Indian Leaf Species Using Smartphones
  • M. Vilasini1, *, P. Ramamoorthy2
1 KPR Institute of Engineering and Technology, Coimbatore, 641407, India.
2 Adithiya Institute of Engineering and Technology, Coimbatore, 641107, India.
* Corresponding Author: M. Vilasini. Email: vilasiniaddress@gmail.com.
Abstract
Leaf species identification leads to multitude of societal applications. There is enormous research in the lines of plant identification using pattern recognition. With the help of robust algorithms for leaf identification, rural medicine has the potential to reappear as like the previous decades. This paper discusses CNN based approaches for Indian leaf species identification from white background using smartphones. Variations of CNN models over the features like traditional shape, texture, color and venation apart from the other miniature features of uniformity of edge patterns, leaf tip, margin and other statistical features are explored for efficient leaf classification.
Keywords
Deep learning, CNN, classification, transfer learning, prewitt edge detection.
Cite This Article
Vilasini, M., Ramamoorthy, P. (2020). CNN Approaches for Classification of Indian Leaf Species Using Smartphones. CMC-Computers, Materials & Continua, 62(3), 1445–1472.