{
  "id": "1802.00030",
  "version": "v1",
  "published": "2018-01-31T19:31:25.000Z",
  "updated": "2018-01-31T19:31:25.000Z",
  "title": "Fusarium Damaged Kernels Detection Using Transfer Learning on Deep Neural Network Architecture",
  "authors": [
    "Márcio Nicolau",
    "Márcia Barrocas Moreira Pimentel",
    "Casiane Salete Tibola",
    "José Mauricio Cunha Fernandes",
    "Willingthon Pavan"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-01-31T19:31:25.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep neural network architecture",
      "fusarium damaged kernels detection",
      "transfer learning",
      "pre-trained deep neural network",
      "small image dataset"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}