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Journal of Electronic Imaging

Performance evaluation of various classifiers for color prediction of rice paddy plant leaf
Author(s): Amandeep Singh; Maninder Lal Singh
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Paper Abstract

The food industry is one of the industries that uses machine vision for a nondestructive quality evaluation of the produce. These quality measuring systems and softwares are precalculated on the basis of various image-processing algorithms which generally use a particular type of classifier. These classifiers play a vital role in making the algorithms so intelligent that it can contribute its best while performing the said quality evaluations by translating the human perception into machine vision and hence machine learning. The crop of interest is rice, and the color of this crop indicates the health status of the plant. An enormous number of classifiers are available to solve the purpose of color prediction, but choosing the best among them is the focus of this paper. Performance of a total of 60 classifiers has been analyzed from the application point of view, and the results have been discussed. The motivation comes from the idea of providing a set of classifiers with excellent performance and implementing them on a single algorithm for the improvement of machine vision learning and, hence, associated applications.

Paper Details

Date Published: 27 April 2016
PDF: 10 pages
J. Electron. Imaging. 25(6) 061403 doi: 10.1117/1.JEI.25.6.061403
Published in: Journal of Electronic Imaging Volume 25, Issue 6
Show Author Affiliations
Amandeep Singh, Guru Nanak Dev Univ. (India)
Maninder Lal Singh, Guru Nanak Dev Univ. (India)

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