Fusarium Damaged Kernels Detection Using Transfer Learning on Deep Neural Network Architecture

Márcio Nicolau, Márcia Barrocas Moreira Pimentel, Casiane Salete Tibola, José Mauricio Cunha Fernandes, Willingthon Pavan

Published 2018-01-31Version 1

The present work shows the application of transfer learning for a pre-trained deep neural network (DNN), using a small image dataset ($\approx$ 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.