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Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks
Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) im...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248543/ https://www.ncbi.nlm.nih.gov/pubmed/34220894 http://dx.doi.org/10.3389/fpls.2021.673505 |
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author | Fernández-Campos, Mariela Huang, Yu-Ting Jahanshahi, Mohammad R. Wang, Tao Jin, Jian Telenko, Darcy E. P. Góngora-Canul, Carlos Cruz, C. D. |
author_facet | Fernández-Campos, Mariela Huang, Yu-Ting Jahanshahi, Mohammad R. Wang, Tao Jin, Jian Telenko, Darcy E. P. Góngora-Canul, Carlos Cruz, C. D. |
author_sort | Fernández-Campos, Mariela |
collection | PubMed |
description | Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained on non-matured and matured spikes images showing the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future. |
format | Online Article Text |
id | pubmed-8248543 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82485432021-07-02 Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks Fernández-Campos, Mariela Huang, Yu-Ting Jahanshahi, Mohammad R. Wang, Tao Jin, Jian Telenko, Darcy E. P. Góngora-Canul, Carlos Cruz, C. D. Front Plant Sci Plant Science Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained on non-matured and matured spikes images showing the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future. Frontiers Media S.A. 2021-06-17 /pmc/articles/PMC8248543/ /pubmed/34220894 http://dx.doi.org/10.3389/fpls.2021.673505 Text en Copyright © 2021 Fernández-Campos, Huang, Jahanshahi, Wang, Jin, Telenko, Góngora-Canul and Cruz. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Fernández-Campos, Mariela Huang, Yu-Ting Jahanshahi, Mohammad R. Wang, Tao Jin, Jian Telenko, Darcy E. P. Góngora-Canul, Carlos Cruz, C. D. Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks |
title | Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks |
title_full | Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks |
title_fullStr | Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks |
title_full_unstemmed | Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks |
title_short | Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks |
title_sort | wheat spike blast image classification using deep convolutional neural networks |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248543/ https://www.ncbi.nlm.nih.gov/pubmed/34220894 http://dx.doi.org/10.3389/fpls.2021.673505 |
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