College of Food Science and Nutritional Engineering, National Engineering Research Centre for Fruits and Vegetables Processing, Key Laboratory of Fruits and Vegetables Processing,Ministry of Agriculture, Engineering Research Centre for Fruits and Vegetables Processing, Ministry of Education, China Agricultural University, Beijing, China
Department of Basic Course Teaching, Vocational and Technical College, Inner Mongolia Agricultural University, Inner Mongolia, China
Corresponding author: Dr. Fang Chen, College of Food Science and Nutritional Engineering, China Agricultural University, No.17, Qinghua East Road, Haidian District, Beijing 100083, P.R. of China. Tel./Fax: +86 10 62737645 18; E-mail: [email protected].
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.
Keywords: Strawberry cultivars, classification, support vector machine, extreme learning machine