Open AccessComparing Convolution Neural Network Models for Leaf Recognition
Author(s) -
Nurbaity Sabri,
Zalilah Abdul Aziz,
Zarina Bibi İbrahim,
Muhammad Akmal Rasydan Bin Mohd Rosni,
Abdul Hafiz bin Abd Ghapul
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.15.17518
Subject(s) - convolutional neural network , computer science , convolution (computer science) , artificial intelligence , pattern recognition (psychology) , artificial neural network , image (mathematics)
This research compares the recognition performance between pre-trained models, GoogLeNet and AlexNet, with basic Convolution Neural Network (CNN) for leaf recognition. Lately, CNN has gained a lot of interest in image processing applications. Numerous pre-trained models have been introduced and the most popular pre-trained models are GoogLeNet and AlexNet. Each model has its own layers of convolution and computational complexity. A great success has been achieved using these classification models in computer vision and this research investigates their performances for leaf recognition using MalayaKew (MK), an open access leaf dataset. GoogLeNet achieves a perfect 100% accuracy, outperforms both AlexNet and basic CNN. On the other hand, the processing time for GoogLeNet is longer compared to the other models due to the high number of layers in its architecture.