Consulta: subjectFacets:"Soft Computing White Wholes (WW) grade cashew kernel images"
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Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks
Provedor de dados: 1
Autores: Ganganagowdar, Narendra Veranagouda; Siddaramappa, Hareesha Katiganere.
 A novel intelligent automated model to recognize and classify a cashew kernels using Artificial Neural Network (ANN). The model primarily intends to work on two phases. The phase one, built with a proposed method to extract features, which includes 16 morphological features and also 24 color features from the input cashew kernel images. In phase two, a Multilayer Perceptron ANN is being used to recognize and classify the given white wholes grades using back propagation learning algorithm. The proposed method achieves a classification accuracy of 88.93%. This study also reveals that the combination of morphological and color features outperforms rather using any one set of features separately to grade cashew kernels. 
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Computer Science and Engineering; Computer Vision; Image Processing; Soft Computing White Wholes (WW) grade cashew kernel images; Feature extraction; Artificial neural networks; Classification.
Ano: 2016
URL: http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861