Cargando…
Leaf disease image retrieval with object detection and deep metric learning
Rapid identification of plant diseases is essential for effective mitigation and control of their influence on plants. For plant disease automatic identification, classification of plant leaf images based on deep learning algorithms is currently the most accurate and popular method. Existing methods...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513793/ https://www.ncbi.nlm.nih.gov/pubmed/36176678 http://dx.doi.org/10.3389/fpls.2022.963302 |
_version_ | 1784798145062895616 |
---|---|
author | Peng, Yingshu Wang, Yi |
author_facet | Peng, Yingshu Wang, Yi |
author_sort | Peng, Yingshu |
collection | PubMed |
description | Rapid identification of plant diseases is essential for effective mitigation and control of their influence on plants. For plant disease automatic identification, classification of plant leaf images based on deep learning algorithms is currently the most accurate and popular method. Existing methods rely on the collection of large amounts of image annotation data and cannot flexibly adjust recognition categories, whereas we develop a new image retrieval system for automated detection, localization, and identification of individual leaf disease in an open setting, namely, where newly added disease types can be identified without retraining. In this paper, we first optimize the YOLOv5 algorithm, enhancing recognition ability in small objects, which helps to extract leaf objects more accurately; secondly, integrating classification recognition with metric learning, jointly learning categorizing images and similarity measurements, thus, capitalizing on prediction ability of available image classification models; and finally, constructing an efficient and nimble image retrieval system to quickly determine leaf disease type. We demonstrate detailed experimental results on three publicly available leaf disease datasets and prove the effectiveness of our system. This work lays the groundwork for promoting disease surveillance of plants applicable to intelligent agriculture and to crop research such as nutrition diagnosis, health status surveillance, and more. |
format | Online Article Text |
id | pubmed-9513793 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95137932022-09-28 Leaf disease image retrieval with object detection and deep metric learning Peng, Yingshu Wang, Yi Front Plant Sci Plant Science Rapid identification of plant diseases is essential for effective mitigation and control of their influence on plants. For plant disease automatic identification, classification of plant leaf images based on deep learning algorithms is currently the most accurate and popular method. Existing methods rely on the collection of large amounts of image annotation data and cannot flexibly adjust recognition categories, whereas we develop a new image retrieval system for automated detection, localization, and identification of individual leaf disease in an open setting, namely, where newly added disease types can be identified without retraining. In this paper, we first optimize the YOLOv5 algorithm, enhancing recognition ability in small objects, which helps to extract leaf objects more accurately; secondly, integrating classification recognition with metric learning, jointly learning categorizing images and similarity measurements, thus, capitalizing on prediction ability of available image classification models; and finally, constructing an efficient and nimble image retrieval system to quickly determine leaf disease type. We demonstrate detailed experimental results on three publicly available leaf disease datasets and prove the effectiveness of our system. This work lays the groundwork for promoting disease surveillance of plants applicable to intelligent agriculture and to crop research such as nutrition diagnosis, health status surveillance, and more. Frontiers Media S.A. 2022-09-13 /pmc/articles/PMC9513793/ /pubmed/36176678 http://dx.doi.org/10.3389/fpls.2022.963302 Text en Copyright © 2022 Peng and Wang. 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 Peng, Yingshu Wang, Yi Leaf disease image retrieval with object detection and deep metric learning |
title | Leaf disease image retrieval with object detection and deep metric learning |
title_full | Leaf disease image retrieval with object detection and deep metric learning |
title_fullStr | Leaf disease image retrieval with object detection and deep metric learning |
title_full_unstemmed | Leaf disease image retrieval with object detection and deep metric learning |
title_short | Leaf disease image retrieval with object detection and deep metric learning |
title_sort | leaf disease image retrieval with object detection and deep metric learning |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513793/ https://www.ncbi.nlm.nih.gov/pubmed/36176678 http://dx.doi.org/10.3389/fpls.2022.963302 |
work_keys_str_mv | AT pengyingshu leafdiseaseimageretrievalwithobjectdetectionanddeepmetriclearning AT wangyi leafdiseaseimageretrievalwithobjectdetectionanddeepmetriclearning |