Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks

• Autor(a) principal: Ganganagowdar, Narendra Veranagouda
• Data de Publicação: 2016
• Outros Autores: Siddaramappa, Hareesha Katiganere
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Acta Scientiarum. Agronomy (Online)
• Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/27861
• topic: White Wholes (WW) grade cashew kernel images; feature extraction; artificial neural networks; classification

 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.