کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
225157 | 464479 | 2008 | 10 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine](/preview/png/225157.png)
Multi-spectral imaging technique was applied to sorting the green tea category. 320 images were captured at three wavelengths (580, 680 and 800 nm) using a multi-spectral digital camera. Entropy values of images were obtained as image texture features. The correction answer rate of least squares-support vector machine (LS-SVM) with radial basis function kernel was up to 100% which was better than those of LS-SVM with linear kernel, partial least squares and radial basis function neural networks, respectively. Results of generation ability test shows that LS-SVM with radial basis function kernel could be effectively used for the application on a few samples. It could be concluded that it is possible to take multi-spectral images of tea and tell which category it is. The whole process is simple, fast, non-destructive and easy to operate.
Journal: Journal of Food Engineering - Volume 88, Issue 4, October 2008, Pages 474–483