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.
|Translated title of the contribution||Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks|
|Number of pages||11|
|Journal||Acta Scientiarum - Agronomy|
|Publication status||Published - 01-04-2016|
All Science Journal Classification (ASJC) codes
- Agronomy and Crop Science