Using machine learning techniques for evaluating tomato ripeness. Issue 4 (March 2015) - El-Bendary, Nashwa; El Hariri, Esraa; Hassanien, Aboul Ella; Badr, Amr
Highlights Egypt occupied the fifth place in both income and weight of tomato production. We proposed an automated multi-class classification approach for tomato ripeness stages. Performance of classification algorithms depends on statistics of the experimented dataset. Training and testing datasets have been generated via employing the 10-fold cross validation. Using OAO multi-class SVMs with linear kernel function outperformed other algorithms.