Authors :
Simriti Koul; Udit Singhania
Volume/Issue :
Volume 5 - 2020, Issue 8 - August
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/2ZO4Beq
DOI :
10.38124/IJISRT20AUG006
Abstract :
We investigate flower species detection on a
large number of classes. In this paper, we try to classify
flower species using 102 flower species dataset offered
by Oxford. Modern search engines provide methods to
visually search for a query image that contains a flower,
but it lacks robustness because of the intra-class
variation among millions of flower species around the
world. So, we use a Deep learning approach using
Convolutional Neural Networks (CNN) to recognize
flower species with high accuracy. We use the Oxford
dataset which was made by the use of electronic items
like a built-in camera in mobile phones and also a
digital camera. Feature extraction of flower images is
performed using a Transfer Learning approach (i.e.
extraction of complex features from a pre-trained
network). We also use image augmentation and image
processing techniques to extract the flower images more
efficiently. After the experimental analysis and using
different pre-trained models, we achieve an accuracy of
85%. Further advancements can be made by using
optimization parameters in the neural nets.
Keywords :
Deep Learning, Artificial Intelligence, Convolutional Neural Networks, Transfer Learning, Flower Recognition, Image Processing
We investigate flower species detection on a
large number of classes. In this paper, we try to classify
flower species using 102 flower species dataset offered
by Oxford. Modern search engines provide methods to
visually search for a query image that contains a flower,
but it lacks robustness because of the intra-class
variation among millions of flower species around the
world. So, we use a Deep learning approach using
Convolutional Neural Networks (CNN) to recognize
flower species with high accuracy. We use the Oxford
dataset which was made by the use of electronic items
like a built-in camera in mobile phones and also a
digital camera. Feature extraction of flower images is
performed using a Transfer Learning approach (i.e.
extraction of complex features from a pre-trained
network). We also use image augmentation and image
processing techniques to extract the flower images more
efficiently. After the experimental analysis and using
different pre-trained models, we achieve an accuracy of
85%. Further advancements can be made by using
optimization parameters in the neural nets.
Keywords :
Deep Learning, Artificial Intelligence, Convolutional Neural Networks, Transfer Learning, Flower Recognition, Image Processing
