Identifying Potato Varieties Using Machine Vision and Artificial Neural Networks
| Autor: | Yousef Abbaspour-Gilandeh, Mahdi Nooshyar, Afshin Azizi, Amirhosein Afkari-Sayah |
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| Rok vydání: | 2015 |
| Předmět: |
Artificial neural network
Computer science business.industry Machine vision Principal (computer security) Image processing 04 agricultural and veterinary sciences Machine learning computer.software_genre Linear discriminant analysis 040401 food science Neural network analysis 0404 agricultural biotechnology Principal component analysis Artificial intelligence business computer Food Science |
| Zdroj: | International Journal of Food Properties. 19:618-635 |
| ISSN: | 1532-2386 1094-2912 |
| DOI: | 10.1080/10942912.2015.1038834 |
| Popis: | The objective of this study is to develop a method for identifying and discriminating 10 potato varieties by combining machine vision and artificial neural network methods. The potato varieties include Agria, Savalan, Florida, Fontaneh, Natasha, Verona, Karso, Elody, Satina, and Emrad. A total number of 72 characteristic parameters specifying color, textural, and morphological features are found among these varieties. By using principal component analysis, 16 principal features are selected for identifying and discriminating potato varieties. The data obtained from image processing were classified using linear discriminant analysis and non-linear artificial neural network method. The accuracy of discriminant analysis were 73.3, 93.3, 73.3, 40, 73.3, 73.3, 66.7, 80, 40, and 53.3%, respectively, for the varieties used in this study. The classification accuracy was improved by 100% for all the varieties using neural network analysis and the correct classification ratio was 100% using this method. It is revea... |
| Databáze: | OpenAIRE |
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