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

2018/01/31 by Márcio Nicolau, M. B. M. Pimentel, Nicolau, Márcio +7
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Chemistry · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Plant Pathogens and Fungal Diseases #Smart Agriculture and AI #Spectroscopy and Chemometric Analyses

paper · pdf · doi:10.48550/arxiv.1802.00030

openalex publication_date 2018/01/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Deep Learning in Neural Networks: An Overview

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