Cargando…
Improved Vision-Based Detection of Strawberry Diseases Using a Deep Neural Network
Detecting plant diseases in the earliest stages, when remedial intervention is most effective, is critical if damage crop quality and farm productivity is to be contained. In this paper, we propose an improved vision-based method of detecting strawberry diseases using a deep neural network (DNN) cap...
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/PMC7874225/ https://www.ncbi.nlm.nih.gov/pubmed/33584739 http://dx.doi.org/10.3389/fpls.2020.559172 |
_version_ | 1783649549064077312 |
---|---|
author | Kim, Byoungjun Han, You-Kyoung Park, Jong-Han Lee, Joonwhoan |
author_facet | Kim, Byoungjun Han, You-Kyoung Park, Jong-Han Lee, Joonwhoan |
author_sort | Kim, Byoungjun |
collection | PubMed |
description | Detecting plant diseases in the earliest stages, when remedial intervention is most effective, is critical if damage crop quality and farm productivity is to be contained. In this paper, we propose an improved vision-based method of detecting strawberry diseases using a deep neural network (DNN) capable of being incorporated into an automated robot system. In the proposed approach, a backbone feature extractor named PlantNet, pre-trained on the PlantCLEF plant dataset from the LifeCLEF 2017 challenge, is installed in a two-stage cascade disease detection model. PlantNet captures plant domain knowledge so well that it outperforms a pre-trained backbone using an ImageNet-type public dataset by at least 3.2% in mean Average Precision (mAP). The cascade detector also improves accuracy by up to 5.25% mAP. The results indicate that PlantNet is one way to overcome the lack-of-annotated-data problem by applying plant domain knowledge, and that the human-like cascade detection strategy effectively improves the accuracy of automated disease detection methods when applied to strawberry plants. |
format | Online Article Text |
id | pubmed-7874225 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78742252021-02-11 Improved Vision-Based Detection of Strawberry Diseases Using a Deep Neural Network Kim, Byoungjun Han, You-Kyoung Park, Jong-Han Lee, Joonwhoan Front Plant Sci Plant Science Detecting plant diseases in the earliest stages, when remedial intervention is most effective, is critical if damage crop quality and farm productivity is to be contained. In this paper, we propose an improved vision-based method of detecting strawberry diseases using a deep neural network (DNN) capable of being incorporated into an automated robot system. In the proposed approach, a backbone feature extractor named PlantNet, pre-trained on the PlantCLEF plant dataset from the LifeCLEF 2017 challenge, is installed in a two-stage cascade disease detection model. PlantNet captures plant domain knowledge so well that it outperforms a pre-trained backbone using an ImageNet-type public dataset by at least 3.2% in mean Average Precision (mAP). The cascade detector also improves accuracy by up to 5.25% mAP. The results indicate that PlantNet is one way to overcome the lack-of-annotated-data problem by applying plant domain knowledge, and that the human-like cascade detection strategy effectively improves the accuracy of automated disease detection methods when applied to strawberry plants. Frontiers Media S.A. 2021-01-11 /pmc/articles/PMC7874225/ /pubmed/33584739 http://dx.doi.org/10.3389/fpls.2020.559172 Text en Copyright © 2021 Kim, Han, Park and Lee. http://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 Kim, Byoungjun Han, You-Kyoung Park, Jong-Han Lee, Joonwhoan Improved Vision-Based Detection of Strawberry Diseases Using a Deep Neural Network |
title | Improved Vision-Based Detection of Strawberry Diseases Using a Deep Neural Network |
title_full | Improved Vision-Based Detection of Strawberry Diseases Using a Deep Neural Network |
title_fullStr | Improved Vision-Based Detection of Strawberry Diseases Using a Deep Neural Network |
title_full_unstemmed | Improved Vision-Based Detection of Strawberry Diseases Using a Deep Neural Network |
title_short | Improved Vision-Based Detection of Strawberry Diseases Using a Deep Neural Network |
title_sort | improved vision-based detection of strawberry diseases using a deep neural network |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7874225/ https://www.ncbi.nlm.nih.gov/pubmed/33584739 http://dx.doi.org/10.3389/fpls.2020.559172 |
work_keys_str_mv | AT kimbyoungjun improvedvisionbaseddetectionofstrawberrydiseasesusingadeepneuralnetwork AT hanyoukyoung improvedvisionbaseddetectionofstrawberrydiseasesusingadeepneuralnetwork AT parkjonghan improvedvisionbaseddetectionofstrawberrydiseasesusingadeepneuralnetwork AT leejoonwhoan improvedvisionbaseddetectionofstrawberrydiseasesusingadeepneuralnetwork |