Using machine learning techniques for evaluating tomato ripeness
作者：
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.
摘要
•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.
论文关键词：Image classification,Features extraction,Ripeness,Principal Component Analysis (PCA),Support Vector Machines (SVMs),Linear Discriminant Analysis (LDA)
论文评审过程：Available online 13 October 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.09.057
