Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/43952
Title: Automated segmentation of powdery mildew disease from cherry leaves using image processing
Authors: Gupta, Varun
Sengar, Namita
Dutta, Malay Kishore
Travieso, Carlos M. 
Alonso, Jesús B. 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Diseases , Agriculture , Image segmentation , Computational efficiency , Algorithm design and analysis , Image recognition, Powdery Mildew , thresholding , Morphological operators
Issue Date: 2017
Journal: 2017 International Work Conference on Bio-Inspired Intelligence: Intelligent Systems for Biodiversity Conservation, IWOBI 2017 - Proceedings
Conference: 5th IEEE International Work Conference on Bio-Inspired Intelligence, IWOBI 2017 
Abstract: An automated detection of plant disease is an important task to find features or abnormalities in plant and its effect on the fruits. In this paper an algorithm is proposed for detection of powdery mildew disease from a cherry leaf images. The proposed method uses an automated strategic removal of background from the image and then extracting the desired diseased portion. A combination of morphological operators and intensity based thresholding are used which creates a method computationally efficient and less complex. A set of public arXiv e-prints data are used to test the proposed algorithm. The tested algorithm achieves accuracy of 99%.
URI: http://hdl.handle.net/10553/43952
ISBN: 9781538608500
DOI: 10.1109/IWOBI.2017.8006454
Source: 2017 International Work Conference on Bio-Inspired Intelligence: Intelligent Systems for Biodiversity Conservation, IWOBI 2017 - Proceedings,v. 2017-January (8006454)
Appears in Collections:Actas de congresos
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