Automated plant identification using artificial neural network and support vector machine. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Kho, Soon Jye; Manickam, Sugumaran; Malek, Sorayya; Mosleh, Mogeeb; Dhillon, Sarinder Kaur
• Is Part Of: Frontiers in life science. Volume 10:Issue 1(2017)
• Page Start: 98
• Page End: 107
• Publication Date: 2017-01-01
• Subjects: Life data technology -- automated species identification -- artificial neural network -- support vector machine -- Ficus; Life sciences -- Periodicals; Biology -- Periodicals; 570.5
• DOI: 10.1080/21553769.2017.1412361
• Languages: English
• ISSNs: 2155-3769

Abstract

ABSTRACT: Ficus is one of the largest genera in plant kingdom reaching to about 1000 species worldwide. While taxonomic keys are available for identifying most species of Ficus, it is very difficult and time consuming for interpretation by a nonprofessional thus requires highly trained taxonomists. The purpose of the current study is to develop an efficient baseline automated system, using image processing with pattern recognition approach, to identify three species of Ficus, which have similar leaf morphology. Leaf images from three different Ficus species namely F. benjamina, F. pellucidopunctata and F. sumatrana were selected. A total of 54 leaf image samples were used in this study. Three main steps that are image pre-processing, feature extraction and recognition were carried out to develop the proposed system. Artificial neural network (ANN) and support vector machine (SVM) were the implemented recognition models. Evaluation results showed the ability of the proposed system to recognize leaf images with an accuracy of 83.3%. However, the ANN model performed slightly better using the AUC evaluation criteria. The system developed in the current study is able to classify the selected Ficus species with acceptable accuracy.