Fusarium Damaged Kernels Detection Using Transfer Learning on Deep Neural Network Architecture. Nicolau, M., Pimentel, M. B. M., Tibola, C. S., Fernandes, J. M. C., & Pavan, W.
abstract   bibtex   
The present work shows the application of transfer learning for a pre-trained deep neural network (DNN), using a small image dataset (≈ 12,000) on a single workstation with enabled NVIDIA GPU card that takes up to 1 hour to complete the training task and archive an overall average accuracy of 94.7%. The DNN presents a 20% score of misclassification for an external test dataset. The accuracy of the proposed methodology is equivalent to ones using HSI methodology (81%-91%) used for the same task, but with the advantage of being independent on special equipment to classify wheat kernel for FHB symptoms.
@article{nicolau_fusarium_nodate,
	title = {Fusarium {Damaged} {Kernels} {Detection} {Using} {Transfer} {Learning} on {Deep} {Neural} {Network} {Architecture}},
	abstract = {The present work shows the application of transfer learning for a pre-trained deep neural network (DNN), using a small image dataset (≈ 12,000) on a single workstation with enabled NVIDIA GPU card that takes up to 1 hour to complete the training task and archive an overall average accuracy of 94.7\%. The DNN presents a 20\% score of misclassification for an external test dataset. The accuracy of the proposed methodology is equivalent to ones using HSI methodology (81\%-91\%) used for the same task, but with the advantage of being independent on special equipment to classify wheat kernel for FHB symptoms.},
	language = {en},
	author = {Nicolau, Marcio and Pimentel, Marcia Barrocas Moreira and Tibola, Casiane Salete and Fernandes, Jose Mauricio Cunha and Pavan, Willingthon},
	pages = {9}
}
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