Matches in Ghent University Academic Bibliography for { <https://biblio.ugent.be/publication/01GMXDFT6JBFXJJD0NC8HGK5YB> ?p ?o. }
Showing items 1 to 21 of
21
with 100 items per page.
- 01GMXDFT6JBFXJJD0NC8HGK5YB classification A1.
- 01GMXDFT6JBFXJJD0NC8HGK5YB date "2022".
- 01GMXDFT6JBFXJJD0NC8HGK5YB language "eng".
- 01GMXDFT6JBFXJJD0NC8HGK5YB type journalArticle.
- 01GMXDFT6JBFXJJD0NC8HGK5YB hasPart 01GMXDNDXNMQAX5GPF5DG6D58F.pdf.
- 01GMXDFT6JBFXJJD0NC8HGK5YB subject "Agriculture and Food Sciences".
- 01GMXDFT6JBFXJJD0NC8HGK5YB doi "10.1016/j.eswa.2022.118240".
- 01GMXDFT6JBFXJJD0NC8HGK5YB issn "0957-4174".
- 01GMXDFT6JBFXJJD0NC8HGK5YB issn "1873-6793".
- 01GMXDFT6JBFXJJD0NC8HGK5YB volume "208".
- 01GMXDFT6JBFXJJD0NC8HGK5YB abstract "Early diagnosis of fusarium head blight (FHB) presence and intensity in wheat can assist decision support for reducing disease spread and minimizing mycotoxin contamination in the grain. Although hyperspectral data was used successfully for the detection of FHB, using the traditional machine learning methods, these rely on time-consuming manual feature extraction, requiring expert skills. This study explores the use of deep learning models which can automatically extract FHB features. Images were generated from single lines hyperspectral (400-750 nm) data collected from wheat canopy in the laboratory and fed to a convolutional neural network (CNN) for pixel classification into two classes of healthy or FHB infected pixels. Four different types of image conversion schemes were explored, which resulted in a spectral (line & bar) graph, compressed spectral line graph and 2D generated band image. Eight different pre-trained lightweight CNN models that require limited computing re-sources were utilized. The preliminary analysis showed that DarkNet 19 model using the spectral line graph images from both smoothed and unsmoothed data resulted in the best accuracy and F1 score of 100 % with a prediction score of 1 for the sample test dataset. The application of feature visualization using an occlusion sensitivity map was able to elucidate the spectral features responsible for the high accuracy for classification. The results suggest the robustness of the developed method for recognition of pixels corresponding to the FHB infected and healthy ears under the laboratory conditions that motivate for potential development with field data.".
- 01GMXDFT6JBFXJJD0NC8HGK5YB author 18213021-b5c9-11eb-b221-93b2f403b961.
- 01GMXDFT6JBFXJJD0NC8HGK5YB author AF1D2850-8822-11E6-BE4C-FF3FB5D1D7B1.
- 01GMXDFT6JBFXJJD0NC8HGK5YB author d30d57a7-9335-11eb-a808-d4a30414454f.
- 01GMXDFT6JBFXJJD0NC8HGK5YB dateCreated "2022-12-22T17:18:26Z".
- 01GMXDFT6JBFXJJD0NC8HGK5YB dateModified "2024-10-29T16:25:54Z".
- 01GMXDFT6JBFXJJD0NC8HGK5YB name "Detection of fusarium head blight in wheat using hyperspectral data and deep learning".
- 01GMXDFT6JBFXJJD0NC8HGK5YB pagination urn:uuid:b7f0f3fa-6952-47eb-9f8c-2c77c1010bf0.
- 01GMXDFT6JBFXJJD0NC8HGK5YB sameAs LU-01GMXDFT6JBFXJJD0NC8HGK5YB.
- 01GMXDFT6JBFXJJD0NC8HGK5YB sourceOrganization urn:uuid:ce595000-4855-47f5-9893-dd1976189951.
- 01GMXDFT6JBFXJJD0NC8HGK5YB type A1.