A Leaf Recognition Approach to Plant Classification Using Machine Learning
Document Type
Conference Paper
Publication Date
12-3-2018
Publication Source
Proceedings of the IEEE National Aerospace Electronics Conference, NAECON
Abstract
The identification of plants is a very important component of workflows in plant ecological research. This paper presents an automated leaf recognition method for plant identification. The proposed technique is simple and computationally efficient. It is based on a combination of two types of texture features, named Bag-of-features (BOF) and Local Binary Pattern (LBP). These features are utilized as inputs to a decision-making model that is based on a multiclass Support Vector Machine (SVM) classifier. The introduced method is evaluated on a publicly available leaf image database. The experimental results demonstrate that our proposed method is the highly efficient technique for plant recognition.
Inclusive pages
431-434
ISBN/ISSN
0547-3578
Publisher
IEEE
Keywords
component, formatting, insert, style, styling, University of Dayton Electro-optics and Photonics
eCommons Citation
Ali, Redha; Hardie, Russell C.; and Essa, Almabrok, "A Leaf Recognition Approach to Plant Classification Using Machine Learning" (2018). Electrical and Computer Engineering Faculty Publications. 434.
https://ecommons.udayton.edu/ece_fac_pub/434
