Abstract
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 |
|---|---|
| Original language | Portuguese |
| Pages (from-to) | 145-155 |
| Number of pages | 11 |
| Journal | Acta Scientiarum - Agronomy |
| Volume | 38 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 01-04-2016 |
All Science Journal Classification (ASJC) codes
- Agronomy and Crop Science
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Ganganagowdar, N. V., & Siddaramappa, H. K. (2016). Detectar e classificar castanhas de caju, tipo inteiro branco, através de rede neural artificial. Acta Scientiarum - Agronomy, 38(2), 145-155. https://doi.org/10.4025/actasciagron.v38i2.27861