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Optical imaging method for determining symptoms severity of cassava mosaic disease

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dc.contributor.author Anderson, Benjamin
dc.contributor.author Eghan, Moses Jojo
dc.contributor.author Asare-Bediako, Elvis
dc.contributor.author Buah-Bassuah, Paul Kingsley
dc.date.accessioned 2021-10-26T09:33:46Z
dc.date.available 2021-10-26T09:33:46Z
dc.date.issued 2015-10-10
dc.identifier.issn 23105496
dc.identifier.uri http://hdl.handle.net/123456789/6264
dc.description 10p:, ill. en_US
dc.description.abstract Cassava mosaic disease (CMD) is a major constraint to cassava production in cassava growing regions. Severity of CMD symptoms on cassava leaves is usually assessed visually using an arbitrary scale, which is semi-qualitative, and does not represent the actual surface area of diseased leaf. The objective of this study was to develop a quantitative method of assessing the severity of CMD. A combination of polarimeteric digital colour images, L*a*b* colour model and K-means clustering algorithm were used to determine the areas of CMD symptoms and healthy areas on leaves. The severity of CMD on a leaf is determined by computing the percentage of the CMD symptomatic area to the total leaf area. The analysis provides relatively fast and accurate classification of Cassava mosaic diseased leaves. The proposed method will enable plant scientists to obtain accurate and reliable data, forming the basis for better decision making en_US
dc.language.iso en en_US
dc.publisher University of Cape Coast en_US
dc.subject Cassava mosaic disease en_US
dc.subject Digital camera en_US
dc.subject Polarimetric image en_US
dc.subject L*a*b* colour model en_US
dc.subject K-means clustering en_US
dc.subject Symptoms severity en_US
dc.title Optical imaging method for determining symptoms severity of cassava mosaic disease en_US
dc.type Article en_US


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