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A Dataset for Forestry Pest Identification
The identification of forest pests is of great significance to the prevention and control of the forest pests' scale. However, existing datasets mainly focus on common objects, which limits the application of deep learning techniques in specific fields (such as agriculture). In this paper, we c...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9331284/ https://www.ncbi.nlm.nih.gov/pubmed/35909784 http://dx.doi.org/10.3389/fpls.2022.857104 |
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author | Liu, Bing Liu, Luyang Zhuo, Ran Chen, Weidong Duan, Rui Wang, Guishen |
author_facet | Liu, Bing Liu, Luyang Zhuo, Ran Chen, Weidong Duan, Rui Wang, Guishen |
author_sort | Liu, Bing |
collection | PubMed |
description | The identification of forest pests is of great significance to the prevention and control of the forest pests' scale. However, existing datasets mainly focus on common objects, which limits the application of deep learning techniques in specific fields (such as agriculture). In this paper, we collected images of forestry pests and constructed a dataset for forestry pest identification, called Forestry Pest Dataset. The Forestry Pest Dataset contains 31 categories of pests and their different forms. We conduct several mainstream object detection experiments on this dataset. The experimental results show that the dataset achieves good performance on various models. We hope that our Forestry Pest Dataset will help researchers in the field of pest control and pest detection in the future. |
format | Online Article Text |
id | pubmed-9331284 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93312842022-07-29 A Dataset for Forestry Pest Identification Liu, Bing Liu, Luyang Zhuo, Ran Chen, Weidong Duan, Rui Wang, Guishen Front Plant Sci Plant Science The identification of forest pests is of great significance to the prevention and control of the forest pests' scale. However, existing datasets mainly focus on common objects, which limits the application of deep learning techniques in specific fields (such as agriculture). In this paper, we collected images of forestry pests and constructed a dataset for forestry pest identification, called Forestry Pest Dataset. The Forestry Pest Dataset contains 31 categories of pests and their different forms. We conduct several mainstream object detection experiments on this dataset. The experimental results show that the dataset achieves good performance on various models. We hope that our Forestry Pest Dataset will help researchers in the field of pest control and pest detection in the future. Frontiers Media S.A. 2022-07-14 /pmc/articles/PMC9331284/ /pubmed/35909784 http://dx.doi.org/10.3389/fpls.2022.857104 Text en Copyright © 2022 Liu, Liu, Zhuo, Chen, Duan 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 Liu, Bing Liu, Luyang Zhuo, Ran Chen, Weidong Duan, Rui Wang, Guishen A Dataset for Forestry Pest Identification |
title | A Dataset for Forestry Pest Identification |
title_full | A Dataset for Forestry Pest Identification |
title_fullStr | A Dataset for Forestry Pest Identification |
title_full_unstemmed | A Dataset for Forestry Pest Identification |
title_short | A Dataset for Forestry Pest Identification |
title_sort | dataset for forestry pest identification |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9331284/ https://www.ncbi.nlm.nih.gov/pubmed/35909784 http://dx.doi.org/10.3389/fpls.2022.857104 |
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