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Plenty fruits are exported from our country such as oranges, apples, mango, grapes etc. Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. Manual identification of defected fruit is very time consuming. But there are few segmentation algorithms that can identify diseases of fruits. In this paper, we suggest a solution for the detection of disease on fruit by using K-means clustering segmentation algorithm. We used color images of fruits for defect segmentation. . Firstly, the colour image is transformed to Lab colour space from RGB. Clustering is then done by taking the absolute difference between each pixel and the clustering centre in Lab colour space. We have taken apple as a case study and evaluated the proposed approach using defected apples. Our results show more than 95% segmentation accuracy for three common diseases on apple.
Keywords:
Image Segmentation
Cite Article:
"Apple Fruit Disease Detection using Image Segmentation Algorithm", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.2, Issue 6, page no.221 - 225, June-2017, Available :http://www.ijrti.org/papers/IJRTI1706043.pdf
Downloads:
000205212
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator