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Field pest monitoring and forecasting system for pest control
Insect pest is an essential factor affecting crop yield, and the effect of pest control depends on the timeliness and accuracy of pest forecasting. The traditional method forecasts pest outbreaks by manually observing (capturing), identifying, and counting insects, which is very time-consuming and l...
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/PMC9465034/ https://www.ncbi.nlm.nih.gov/pubmed/36105712 http://dx.doi.org/10.3389/fpls.2022.990965 |
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author | Liu, Chengkang Zhai, Zhiqiang Zhang, Ruoyu Bai, Jingya Zhang, Mengyun |
author_facet | Liu, Chengkang Zhai, Zhiqiang Zhang, Ruoyu Bai, Jingya Zhang, Mengyun |
author_sort | Liu, Chengkang |
collection | PubMed |
description | Insect pest is an essential factor affecting crop yield, and the effect of pest control depends on the timeliness and accuracy of pest forecasting. The traditional method forecasts pest outbreaks by manually observing (capturing), identifying, and counting insects, which is very time-consuming and laborious. Therefore, developing a method that can more timely and accurately identify insects and obtain insect information. This study designed an image acquisition device that can quickly collect real-time photos of phototactic insects. A pest identification model was established based on a deep learning algorithm. In addition, a model update strategy and a pest outbreak warning method based on the identification results were proposed. Insect images were processed to establish the identification model by removing the background; a laboratory image collection test verified the feasibility. The results showed that the proportion of images with the background completely removed was 90.2%. Dataset 1 was obtained using reared target insects, and the identification accuracy of the ResNet V2 model on the test set was 96%. Furthermore, Dataset 2 was obtained in the cotton field using a designed field device. In exploring the model update strategy, firstly, the T_ResNet V2 model was trained with Dataset 2 using transfer learning based on the ResNet V2 model; its identification accuracy on the test set was 84.6%. Secondly, after reasonably mixing the indoor and field datasets, the SM_ResNet V2 model had an identification accuracy of 85.7%. The cotton pest image acquisition, transmission, and automatic identification system provide a good tool for accurately forecasting pest outbreaks in cotton fields. |
format | Online Article Text |
id | pubmed-9465034 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94650342022-09-13 Field pest monitoring and forecasting system for pest control Liu, Chengkang Zhai, Zhiqiang Zhang, Ruoyu Bai, Jingya Zhang, Mengyun Front Plant Sci Plant Science Insect pest is an essential factor affecting crop yield, and the effect of pest control depends on the timeliness and accuracy of pest forecasting. The traditional method forecasts pest outbreaks by manually observing (capturing), identifying, and counting insects, which is very time-consuming and laborious. Therefore, developing a method that can more timely and accurately identify insects and obtain insect information. This study designed an image acquisition device that can quickly collect real-time photos of phototactic insects. A pest identification model was established based on a deep learning algorithm. In addition, a model update strategy and a pest outbreak warning method based on the identification results were proposed. Insect images were processed to establish the identification model by removing the background; a laboratory image collection test verified the feasibility. The results showed that the proportion of images with the background completely removed was 90.2%. Dataset 1 was obtained using reared target insects, and the identification accuracy of the ResNet V2 model on the test set was 96%. Furthermore, Dataset 2 was obtained in the cotton field using a designed field device. In exploring the model update strategy, firstly, the T_ResNet V2 model was trained with Dataset 2 using transfer learning based on the ResNet V2 model; its identification accuracy on the test set was 84.6%. Secondly, after reasonably mixing the indoor and field datasets, the SM_ResNet V2 model had an identification accuracy of 85.7%. The cotton pest image acquisition, transmission, and automatic identification system provide a good tool for accurately forecasting pest outbreaks in cotton fields. Frontiers Media S.A. 2022-08-29 /pmc/articles/PMC9465034/ /pubmed/36105712 http://dx.doi.org/10.3389/fpls.2022.990965 Text en Copyright © 2022 Liu, Zhai, Zhang, Bai 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 Liu, Chengkang Zhai, Zhiqiang Zhang, Ruoyu Bai, Jingya Zhang, Mengyun Field pest monitoring and forecasting system for pest control |
title | Field pest monitoring and forecasting system for pest control |
title_full | Field pest monitoring and forecasting system for pest control |
title_fullStr | Field pest monitoring and forecasting system for pest control |
title_full_unstemmed | Field pest monitoring and forecasting system for pest control |
title_short | Field pest monitoring and forecasting system for pest control |
title_sort | field pest monitoring and forecasting system for pest control |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9465034/ https://www.ncbi.nlm.nih.gov/pubmed/36105712 http://dx.doi.org/10.3389/fpls.2022.990965 |
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