Automatic Flower Disease Identification Using Image Processing
No Thumbnail Available
Date
2015-02
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa University
Abstract
Currently, the cultivation of flowers is becoming popular. However, during the cultivation
process there may be a number of challenges that affect it, one of which is flower disease.
Most flower diseases are caused by insects, fungi, and bacteria. Identification of these
diseases need experienced experts in this area. Thus, developing a system that
automatically identifies flower diseases can help to support the experienced experts.
In view of this, an image processing based system for automatic identification of flower
disease is proposed. The proposed system consists of two main phases. In the first phase
normal and diseased flower image are used to create a knowledge base. During the creation
of the knowledge base, images are pre-processed and segmented to identify the region of
interest. Then, seven different texture features of images are extracted using Gabor texture
feature extraction. Finally, an artificial neural network is trained using seven input features
extracted from the individual image and eight output vectors that represent eight different
classes of disease to represent the knowledge base. In the second phase, the knowledge
base is used to identify the disease of a flower.
In order to create the knowledge base and to test the effectiveness of the developed system,
we have used 40 flower images for each of the eight different classes of flower disease and
we have a total of 320 flower images. From those images 85% of the Dataset is used for
training and 15% of the data set is used for testing. The experimental result demonstrates
that the proposed technique is effective technique for the identification of flower disease.
The developed system can successfully identify the examined flower with an accuracy of
83.3%.
Keywords: Gabor Feature Extraction, Artificial Neural Network, Texture Feature
Description
Keywords
Gabor Feature Extraction; Artificial Neural Network;Texture Feature