| Autor: |
Tan Kezhu, Chai Yuhua, Song Weixian, Cao Xiaoda |
| Předmět: |
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| Zdroj: |
Transactions of the Chinese Society of Agricultural Engineering; May2014, Vol. 30 Issue 9, p235-242, 8p |
| Abstrakt: |
Different soybean seed varieties have different components (oil, protein, fat etc.) content. Identification of soybean seed varieties is a critical factor that improves the quality of produced soybean. In this study, hyperspectral image technique was applied in order to classify soybean seeds based on their varieties. The spectral reflectance data was collected using the optical sensor system with spectral region of 1000-2500nm. Principal component analysis (PCA) was performed to reduce the dimensionality of the data and remove the redundancy. Scores of four PCs were used as input features in the classification algorithm. Four texture feature parameters (angular second moment, energy, entropy and correlation) were extracted from each feature image selected by PCA. For the extraction of specific features, four significant feature parameters were computed from the 16 characteristic variables. Artificial neural network (ANN) classifier was employed for classification using top selected features. The obtained average training accuracy rate was 97.50% and the average testing accuracy rate was 93.88%. Thus, the results confirmed that hyperspectral image technique in-conjunction with BP neural network could be useful for soybean seed varieties classification. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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