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
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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.