Validation of a Neural Network Based Leaf Classification Algorithm
Main Article Content
Abstract
Plants are living organism which belongs to vegetable kingdom that can live both on land and water. More than 300,000 species of
plants exists on earth. In order to effectively conserve and save the genetic resources, plants are to be identified and leaf shape plays a significant
role in plant classification. In this paper, since identifying the relevant feature is of vital importance the features are extracted using information
gain based feature selection method. A feed forward neural networks with different learning methods viz., Levenberg-Marquardt learning,
Incremental Backpropagation learning and Batch Back propogation learning automate the leaf recongnition for plant classification. Comparison
shows that information gain helps select features that show good improvement on feed forward neural network (normalized cubic spline) with
batch back propogation classifier algorithm out performs with an accuracy of 95.56%.
Â
Key words: Information gain, Levenberg-Marquardt, Incremental Backpropagation, Batch Back propogation, Feed Forward Neural Network
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.