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Image-Based Hot Pepper Disease and Pest Diagnosis Using Transfer Learning and Fine-Tuning
Past studies of plant disease and pest recognition used classification methods that presented a singular recognition result to the user. Unfortunately, incorrect recognition results may be output, which may lead to further crop damage. To address this issue, there is a need for a system that suggest...
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/PMC8716927/ https://www.ncbi.nlm.nih.gov/pubmed/34975933 http://dx.doi.org/10.3389/fpls.2021.724487 |
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author | Gu, Yeong Hyeon Yin, Helin Jin, Dong Park, Jong-Han Yoo, Seong Joon |
author_facet | Gu, Yeong Hyeon Yin, Helin Jin, Dong Park, Jong-Han Yoo, Seong Joon |
author_sort | Gu, Yeong Hyeon |
collection | PubMed |
description | Past studies of plant disease and pest recognition used classification methods that presented a singular recognition result to the user. Unfortunately, incorrect recognition results may be output, which may lead to further crop damage. To address this issue, there is a need for a system that suggest several candidate results and allow the user to make the final decision. In this study, we propose a method for diagnosing plant diseases and identifying pests using deep features based on transfer learning. To extract deep features, we employ pre-trained VGG and ResNet 50 architectures based on the ImageNet dataset, and output disease and pest images similar to a query image via a k-nearest-neighbor algorithm. In this study, we use a total of 23,868 images of 19 types of hot-pepper diseases and pests, for which, the proposed model achieves accuracies of 96.02 and 99.61%, respectively. We also measure the effects of fine-tuning and distance metrics. The results show that the use of fine-tuning-based deep features increases accuracy by approximately 0.7–7.38%, and the Bray–Curtis distance achieves an accuracy of approximately 0.65–1.51% higher than the Euclidean distance. |
format | Online Article Text |
id | pubmed-8716927 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87169272021-12-31 Image-Based Hot Pepper Disease and Pest Diagnosis Using Transfer Learning and Fine-Tuning Gu, Yeong Hyeon Yin, Helin Jin, Dong Park, Jong-Han Yoo, Seong Joon Front Plant Sci Plant Science Past studies of plant disease and pest recognition used classification methods that presented a singular recognition result to the user. Unfortunately, incorrect recognition results may be output, which may lead to further crop damage. To address this issue, there is a need for a system that suggest several candidate results and allow the user to make the final decision. In this study, we propose a method for diagnosing plant diseases and identifying pests using deep features based on transfer learning. To extract deep features, we employ pre-trained VGG and ResNet 50 architectures based on the ImageNet dataset, and output disease and pest images similar to a query image via a k-nearest-neighbor algorithm. In this study, we use a total of 23,868 images of 19 types of hot-pepper diseases and pests, for which, the proposed model achieves accuracies of 96.02 and 99.61%, respectively. We also measure the effects of fine-tuning and distance metrics. The results show that the use of fine-tuning-based deep features increases accuracy by approximately 0.7–7.38%, and the Bray–Curtis distance achieves an accuracy of approximately 0.65–1.51% higher than the Euclidean distance. Frontiers Media S.A. 2021-12-16 /pmc/articles/PMC8716927/ /pubmed/34975933 http://dx.doi.org/10.3389/fpls.2021.724487 Text en Copyright © 2021 Gu, Yin, Jin, Park and Yoo. 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 Gu, Yeong Hyeon Yin, Helin Jin, Dong Park, Jong-Han Yoo, Seong Joon Image-Based Hot Pepper Disease and Pest Diagnosis Using Transfer Learning and Fine-Tuning |
title | Image-Based Hot Pepper Disease and Pest Diagnosis Using Transfer Learning and Fine-Tuning |
title_full | Image-Based Hot Pepper Disease and Pest Diagnosis Using Transfer Learning and Fine-Tuning |
title_fullStr | Image-Based Hot Pepper Disease and Pest Diagnosis Using Transfer Learning and Fine-Tuning |
title_full_unstemmed | Image-Based Hot Pepper Disease and Pest Diagnosis Using Transfer Learning and Fine-Tuning |
title_short | Image-Based Hot Pepper Disease and Pest Diagnosis Using Transfer Learning and Fine-Tuning |
title_sort | image-based hot pepper disease and pest diagnosis using transfer learning and fine-tuning |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8716927/ https://www.ncbi.nlm.nih.gov/pubmed/34975933 http://dx.doi.org/10.3389/fpls.2021.724487 |
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