Leaf Disease Detection Using Image Processing and Support Vector Machine (SVM)
Author(s):
Vaijinath B. Batule , Dept. of Information Technology, Trinity College of Engineering And Research,Pune, Maharashtra,India; Kiran D. Wadkar, Dept. of Information Technology, Trinity College of Engineering And Research,Pune, Maharashtra,India; Gaurav U. Chavan, Dept. of Information Technology, Trinity College of Engineering And Research,Pune, Maharashtra,India; Vishal P. Sanap, Dept. of Information Technology, Trinity College of Engineering And Research,Pune, Maharashtra,IndiaKeywords:
Leaf disease, Image processing, Feature extraction, k-means, Support Vector MachineAbstract:
in the study on leaf disease detection can be a helpful aspect in keeping an eye on huge area of fields of crops, but it’s important to detect the disease as early as possible. This paper gives a method to detect the disease caused to the leaf calculating the RGB and HSV values. Primarily the image is blurred in order reduce noise. Then the image is converted from RGB to HSV form, after this color thresholding is done. After thresholding foreground or background detection is performed. Background detection leads to feature extractions of the leaf. Then k-means algorithm is applied which can help to clot the clusters. The following system is a software based solution for detecting the disease with which the leaf is infected. In order to detect the disease some steps are to be followed using image processing and support vector machine. Improving the quality and production of agricultural products detection of the leaf disease can be useful.
Other Details:
| Manuscript Id | : | J4RV2I2033 |
| Published in | : | Volume : 2, Issue : 2 |
| Publication Date | : | 01/05/2016 |
| Page(s) | : | 74-77 |
