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.
| 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 |
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All Science Journal Classification (ASJC) codes
- Agronomy and Crop Science
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Detectar e classificar castanhas de caju, tipo inteiro branco, através de rede neural artificial. / Ganganagowdar, Narendra Veranagouda; Siddaramappa, Hareesha Katiganere.
In: Acta Scientiarum - Agronomy, Vol. 38, No. 2, 01.04.2016, p. 145-155.Research output: Contribution to journal › Article
TY - JOUR
T1 - Detectar e classificar castanhas de caju, tipo inteiro branco, através de rede neural artificial
AU - Ganganagowdar, Narendra Veranagouda
AU - Siddaramappa, Hareesha Katiganere
PY - 2016/4/1
Y1 - 2016/4/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84963854390&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963854390&partnerID=8YFLogxK
U2 - 10.4025/actasciagron.v38i2.27861
DO - 10.4025/actasciagron.v38i2.27861
M3 - Article
AN - SCOPUS:84963854390
VL - 38
SP - 145
EP - 155
JO - Acta Scientiarum - Agronomy
JF - Acta Scientiarum - Agronomy
SN - 1679-9275
IS - 2
ER -