A Plant Recognition Approach Using Shape and Color Features in Leaf Images

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ÇAĞLAYAN A., GÜÇLÜ O., CAN A. B.

17th International Conference on Image Analysis and Processing (ICIAP), Naples, Italy, 9 - 13 September 2013, vol.8157, pp.161-170 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 8157
  • Doi Number: 10.1007/978-3-642-41184-7_17
  • City: Naples
  • Country: Italy
  • Page Numbers: pp.161-170
  • Hacettepe University Affiliated: Yes

Abstract

Recognizing plants is a vital problem especially for biologists, chemists, and environmentalists. Plant recognition can be performed by human experts manually but it is a time consuming and low-efficiency process. Automation of plant recognition is an important process for the fields working with plants. This paper presents an approach for plant recognition using leaf images. Shape and color features extracted from leaf images are used with k-Nearest Neighbor, Support Vector Machines, Naive Bayes, and Random Forest classification algorithms to recognize plant types. The presented approach is tested on 1897 leaf images and 32 kinds of leaves. The results demonstrated that success rate of plant recognition can be improved up to 96% with Random Forest method when both shape and color features are used.