Leaf Diseases Detection of Medicinal Plants Based on Support Vector Machine Classification Algorithm
Payal Bose
Lincoln University College, Kota Bharu, Kelantan, Malaysia.
Shawni Dutta
Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India.
Vishal Goyal
GLA University, Mathura-Delhi Road Mathura, Chaumuhan, Uttar Pradesh, India.
Samir K. Bandyopadhyay *
Lincoln University College, Kota Bharu, Kelantan, Malaysia.
*Author to whom correspondence should be addressed.
Abstract
On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. The different species of plants have different diseases. Therefore, it is required to identify the plants as well as their diseases correctly. It is difficult and also time consuming to identify the plants and their diseases manually. In this research an automatic disease detection system of plant is proposed. High-quality leaf images are used for training and testing. For detecting the healthy area and diseased area in a leaf, region-based and color-based region thresholding techniques are used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally, for classification two-class and multi-class Support Vector Machine (SVM) were used. It is found that both feature selection processes with SVM give 99% accuracy. An user oriented graphical user interface is created for understanding the automated system.
Keywords: Image processing, automated plant diseases detection, histogram oriented gradient (HOG), local binary pattern (LBP), support vector machine (SVM).