AIRCC's International Journal of Computer Science and Information Technology

The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader).

If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs.

Alternatively, you can download the PDF file directly to your computer, from where it can be opened using a PDF reader. To download the PDF, click the Download link above.

Fullscreen Fullscreen Off


This paper introduces an approach of plant classification which is based on the characterization of texture properties. We used the combined classifier learning vector quantization. We randomly took out 30 blocks of each texture as a training set and another 30 blocks as a testing set. We found that the combined classifier method gave a high performance which is a superior than other tested methods. The experimental results indicated that our algorithm is applicable and its average correct recognition rate was 98.7%.

Keywords

Plant Classification, Learning Vector Quantization(LVQ), Radial Base Function(RBF), Texture Classification, Neural Networks, Texture Classification.
User
Notifications
Font Size