Using machine learning techniques for evaluating tomato ripeness. Issue 4 (March 2015)

• Record Type: Journal Article
• Authors: El-Bendary, Nashwa; El Hariri, Esraa; Hassanien, Aboul Ella; Badr, Amr
• Is Part Of: Expert systems with applications. Volume 42:Issue 4(2015)
• Page Start: 1892
• Page End: 1905
• Publication Date: 2015-03
• Subjects: Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.09.057
• Languages: English
• ISSNs: 0957-4174

Abstract

<abstract xml:lang="en" abstract-type="author-highlights" id="ab005"> <title id="st105">Highlights</title> <sec> <p id="sp005"> <list id="l0005"> <list-item id="u0005"> <label></label> <p id="p0360">Egypt occupied the fifth place in both income and weight of tomato production.</p> </list-item> <list-item id="u0010"> <label></label> <p id="p0365">We proposed an automated multi-class classification approach for tomato ripeness stages.</p> </list-item> <list-item id="u0015"> <label></label> <p id="p0370">Performance of classification algorithms depends on statistics of the experimented dataset.</p> </list-item> <list-item id="u0020"> <label></label> <p id="p0375">Training and testing datasets have been generated via employing the 10-fold cross validation.</p> </list-item> <list-item id="u0025"> <label></label> <p id="p0380">Using OAO multi-class SVMs with linear kernel function outperformed other algorithms.</p> </list-item> </list> </p> </sec> </abstract>