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Automated Plant Disease Analysis
Authors(4) :-Bhawana Moon, Nigkhat Sheikh, Shahana Sadaf, Amrin Khan
We propose and experimentally evaluate a software solution for automatic detection and classification of plant leaf diseases. The proposed solution is an improvement to the solution proposed in  as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in . The following two steps are added successively after the segmentation phase. In the first step we identify the mostly green coloured pixels. Next, these pixels are masked based on specific threshold values that are computed using Otsu's method, then those mostly green pixels are masked. The other additional step is that the pixels with zeros red, green and blue values and the pixels on the boundaries of the infected cluster (object) were completely removed. The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases. The developed algorithms efficiency can successfully detect and classify the examined diseases with a precision between 83% and 94%, and can achieve 20% speedup over the approach proposed in . General Terms Artificial Intelligence, Image Processing.
Bhawana Moon, Nigkhat Sheikh, Shahana Sadaf, Amrin Khan
Image processing; Plant disease detection; Classification
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Published in : Volume 3 | Issue 2 | January-February 2017
Date of Publication : 2017-02-28
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 30-34
Manuscript Number : NCAEAS2309
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Bhawana Moon, Nigkhat Sheikh, Shahana Sadaf, Amrin Khan, "Automated Plant Disease Analysis", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 2, pp.30-34 , January-February-2017
URL : http://ijsrst.com/NCAEAS2309