Skip to main content
SpringerLink
Log in

Species classification of aquatic plants using GRNN and BPNN

  • Original Article
  • Published:
AI & SOCIETY Aims and scope Submit manuscript

Abstract

Computer-aided plant species identification acts significantly on plant digital museum system and systematic botany, which is the groundwork for research and development of plants. This work presents a method for plant species identification using the images of flowers. It focuses on the stable feature extraction of flowers such as color, texture and shape features. Color-based segmentation using k-means clustering is used to extract the color features. Texture segmentation using texture filter is used to segment the image and obtain texture features. Sobel, Prewitt and Robert operators are used to extract the boundary of image and to obtain the shape features. From 405 images of flowers, color, texture and shape features are extracted. Classification of the plants into dry land plants and aquatic plants, the aquatic plant species into wet and marsh aquatic plants, wet aquatic plants into Iridaceae and Epilobium family and marsh aquatic plants into Malvaceae and Onagraceae family, the Iridaceae family is again classified into Babiana and Crocus species, the family Epilobium into Canum and Hirsutum, the family Malvaceae into Mallow and Pavonia, the family Onagraceae into Fuschia and Ludwigia species are done using general regression neural network and backpropagation neural network classifiers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Fu H, Chil Z, Fengl D, Song J (2004) Machine learning techniques for ontology-based leaf classification. In: 8th international conference on control, automation, robotics and vision Kunming, China, vol 1, pp 681–686, 6–9th Dec 2004

  • Gonzalez RC, Woods RE (2008) Digital image processing using Matlab. PHI Learning Private Limited, New Delhi

    Google Scholar 

  • Kebapci H, Yanikoglu B, Unal G (2010) ``Plant image retrieval using color, shape and texture features’’, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey. The Comput J Adv Access 54(9):1475–1490

  • Lin H, Peng H (2008) Machine recognition for broad-leaved trees based on synthetic features of leaves using probabilistic neural network. In: International conference on computer science and software engineering, pp 871–877

  • Ma WY, Deng Y, Manjunath BS (1997) Tools for texture/color based search of images. In: SPIE International Conference 3106, Human Vision and Electronic Imaging II, Department of Electrical and Computer Engineering, University of California, Santa Barbara, pp 496–507, Feb, 1997

  • Martin DR, Fowlkes CC, Malik J (2004) Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans Pattern Anal Mach Intell 26(5):530–549

    Article  Google Scholar 

  • Matlab Image Processing Toolbox™ 7 user’s guide, version 7.2 (Release 2011a)

  • Norma Jean (1914) Venable illustrated by Ann Payne “AQUATIC PLANTS- guide to aquatic and wetland plants of West Virginia”. Cooperative Extension Service West Virginia University Extension and Public Service series 803

  • Pan J, Hangzhou, Panjz Z (2008) “Recognition of plants by leaves digital image and neural network”. Yong He Biosystem Engineering and Food Science College, Zhejiang University, pp 906–910

  • Rahmadhani M, Herdiyeni Y (2010) Shape and vein extraction on plant leaf images using fourier and B-spline modeling. In: AFITA international conference, the quality information for competitive agricultural based production system and commerce, pp 306–310

  • Sathya Bama B, Mohana Valli S, Raju S, Abhai V (2011) Content based leaf image retrieval (CBLIR) using shape, color and texture features. Indian J Comput Sci Eng 2(2):202–211

    Google Scholar 

  • Zhai C-M, Du J-X (2008) Applying extreme learning machine to plant species identification. In: Proceedings of the IEEE international conference on information and automation, Zhangjiajie, pp 879–884, June 20–23, 2008

  • Zhang L, Kong J, Zeng X, Ren J (2008) Plant species identification based on neural network. In: Fourth international conference on natural computation. IEEE Comput Soc 5:90–94

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Annamalai University, Chidambaram, India

    S. Abirami, V. Ramalingam & S. Palanivel

Authors
  1. S. Abirami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Ramalingam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Palanivel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Abirami.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Abirami, S., Ramalingam, V. & Palanivel, S. Species classification of aquatic plants using GRNN and BPNN. AI & Soc 29, 45–52 (2014). https://doi.org/10.1007/s00146-012-0433-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00146-012-0433-z

Keywords

Search

Navigation