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

Creative Commons License

ÇAĞLAYAN A., GÜÇLÜ O., CAN A. B.

17th International Conference on Image Analysis and Processing (ICIAP), Naples, İtalya, 9 - 13 Eylül 2013, cilt.8157, ss.161-170 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 8157
  • Doi Numarası: 10.1007/978-3-642-41184-7_17
  • Basıldığı Şehir: Naples
  • Basıldığı Ülke: İtalya
  • Sayfa Sayıları: ss.161-170
  • Hacettepe Üniversitesi Adresli: Evet

Özet

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.