Cargando…
Hyperspectral Imaging Combined With Deep Transfer Learning for Rice Disease Detection
Various rice diseases threaten the growth of rice. It is of great importance to achieve the rapid and accurate detection of rice diseases for precise disease prevention and control. Hyperspectral imaging (HSI) was performed to detect rice leaf diseases in four different varieties of rice. Considerin...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8511421/ https://www.ncbi.nlm.nih.gov/pubmed/34659278 http://dx.doi.org/10.3389/fpls.2021.693521 |
_version_ | 1784582758379552768 |
---|---|
author | Feng, Lei Wu, Baohua He, Yong Zhang, Chu |
author_facet | Feng, Lei Wu, Baohua He, Yong Zhang, Chu |
author_sort | Feng, Lei |
collection | PubMed |
description | Various rice diseases threaten the growth of rice. It is of great importance to achieve the rapid and accurate detection of rice diseases for precise disease prevention and control. Hyperspectral imaging (HSI) was performed to detect rice leaf diseases in four different varieties of rice. Considering that it costs much time and energy to develop a classifier for each variety of rice, deep transfer learning was firstly introduced to rice disease detection across different rice varieties. Three deep transfer learning methods were adapted for 12 transfer tasks, namely, fine-tuning, deep CORrelation ALignment (CORAL), and deep domain confusion (DDC). A self-designed convolutional neural network (CNN) was set as the basic network of the deep transfer learning methods. Fine-tuning achieved the best transferable performance with an accuracy of over 88% for the test set of the target domain in the majority of transfer tasks. Deep CORAL obtained an accuracy of over 80% in four of all the transfer tasks, which was superior to that of DDC. A multi-task transfer strategy has been explored with good results, indicating the potential of both pair-wise, and multi-task transfers. A saliency map was used for the visualization of the key wavelength range captured by CNN with and without transfer learning. The results indicated that the wavelength range with and without transfer learning was overlapped to some extent. Overall, the results suggested that deep transfer learning methods could perform rice disease detection across different rice varieties. Hyperspectral imaging, in combination with the deep transfer learning method, is a promising possibility for the efficient and cost-saving field detection of rice diseases among different rice varieties. |
format | Online Article Text |
id | pubmed-8511421 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85114212021-10-14 Hyperspectral Imaging Combined With Deep Transfer Learning for Rice Disease Detection Feng, Lei Wu, Baohua He, Yong Zhang, Chu Front Plant Sci Plant Science Various rice diseases threaten the growth of rice. It is of great importance to achieve the rapid and accurate detection of rice diseases for precise disease prevention and control. Hyperspectral imaging (HSI) was performed to detect rice leaf diseases in four different varieties of rice. Considering that it costs much time and energy to develop a classifier for each variety of rice, deep transfer learning was firstly introduced to rice disease detection across different rice varieties. Three deep transfer learning methods were adapted for 12 transfer tasks, namely, fine-tuning, deep CORrelation ALignment (CORAL), and deep domain confusion (DDC). A self-designed convolutional neural network (CNN) was set as the basic network of the deep transfer learning methods. Fine-tuning achieved the best transferable performance with an accuracy of over 88% for the test set of the target domain in the majority of transfer tasks. Deep CORAL obtained an accuracy of over 80% in four of all the transfer tasks, which was superior to that of DDC. A multi-task transfer strategy has been explored with good results, indicating the potential of both pair-wise, and multi-task transfers. A saliency map was used for the visualization of the key wavelength range captured by CNN with and without transfer learning. The results indicated that the wavelength range with and without transfer learning was overlapped to some extent. Overall, the results suggested that deep transfer learning methods could perform rice disease detection across different rice varieties. Hyperspectral imaging, in combination with the deep transfer learning method, is a promising possibility for the efficient and cost-saving field detection of rice diseases among different rice varieties. Frontiers Media S.A. 2021-09-29 /pmc/articles/PMC8511421/ /pubmed/34659278 http://dx.doi.org/10.3389/fpls.2021.693521 Text en Copyright © 2021 Feng, Wu, He and Zhang. 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 Feng, Lei Wu, Baohua He, Yong Zhang, Chu Hyperspectral Imaging Combined With Deep Transfer Learning for Rice Disease Detection |
title | Hyperspectral Imaging Combined With Deep Transfer Learning for Rice Disease Detection |
title_full | Hyperspectral Imaging Combined With Deep Transfer Learning for Rice Disease Detection |
title_fullStr | Hyperspectral Imaging Combined With Deep Transfer Learning for Rice Disease Detection |
title_full_unstemmed | Hyperspectral Imaging Combined With Deep Transfer Learning for Rice Disease Detection |
title_short | Hyperspectral Imaging Combined With Deep Transfer Learning for Rice Disease Detection |
title_sort | hyperspectral imaging combined with deep transfer learning for rice disease detection |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8511421/ https://www.ncbi.nlm.nih.gov/pubmed/34659278 http://dx.doi.org/10.3389/fpls.2021.693521 |
work_keys_str_mv | AT fenglei hyperspectralimagingcombinedwithdeeptransferlearningforricediseasedetection AT wubaohua hyperspectralimagingcombinedwithdeeptransferlearningforricediseasedetection AT heyong hyperspectralimagingcombinedwithdeeptransferlearningforricediseasedetection AT zhangchu hyperspectralimagingcombinedwithdeeptransferlearningforricediseasedetection |