Data/hora: 13/06/2024 04:47:12

Provedor de dados: | 1
País: | Brazil
Título: | Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks
Autores: | Ganganagowdar, Narendra Veranagouda; Siddaramappa, Hareesha Katiganere.
Data: | 2016-04-01
Ano: | 2016
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.
Resumo: | 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
Idioma: | Inglês

Identificador:

http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861

10.4025/actasciagron.v38i2.27861

Editor: | Universidade Estadual de Maringá

Relação:

http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861/pdf_109

http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/downloadSuppFile/27861/10080

http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/downloadSuppFile/27861/10082

http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/downloadSuppFile/27861/10083

Formato: | application/pdf

Fonte:

Acta Scientiarum. Agronomy; v. 38, n. 2 (2016); 145-155

Acta Scientiarum. Agronomy; v. 38, n. 2 (2016); 145-155

1807-8621

1679-9275