Classification of fresh and processed strawberry cultivars based on quality characteristics by using support vector machine and extreme learning machine

Abstract

BACKGROUND: Classification of fresh and processing strawberry cultivars is important to make the best utilization of different cultivars in processing. The aim of the study was to investigate whether support vector machine (SVM) and extreme learning machine (ELM) could assist the classification of 15 strawberry cultivars. Twenty-two characteristic indexes were analyzed, including not only appearance indexes but also nutritional indexes. RESULTS: The results showed that classification accuracies of 100% and 88.52% were obtained by using SVM and ELM with 3-fold cross validation, respectively. Moreover, seven characteristic variables extracted from 22 quality indexes by SVM could make it possible to determine the adaptability of a particular cultivar by measuring relatively small number of indexes. CONCLUSION: Both ELM and SVM models are feasible to identify fresh and processing cultivars. However, SVM showed better performance for its accuracy and simplicity, indicating that SVM would be a good choice for classification of strawberry cultivars.