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A YOLOv7 incorporating the Adan optimizer based corn pests identification method

Major pests of corn insects include corn borer, armyworm, bollworm, aphid, and corn leaf mites. Timely and accurate detection of these pests is crucial for effective pests control and scientific decision making. However, existing methods for identification based on traditional machine learning and n...

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Autores principales: Zhang, Chong, Hu, Zhuhua, Xu, Lewei, Zhao, Yaochi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10277678/
https://www.ncbi.nlm.nih.gov/pubmed/37342143
http://dx.doi.org/10.3389/fpls.2023.1174556
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author Zhang, Chong
Hu, Zhuhua
Xu, Lewei
Zhao, Yaochi
author_facet Zhang, Chong
Hu, Zhuhua
Xu, Lewei
Zhao, Yaochi
author_sort Zhang, Chong
collection PubMed
description Major pests of corn insects include corn borer, armyworm, bollworm, aphid, and corn leaf mites. Timely and accurate detection of these pests is crucial for effective pests control and scientific decision making. However, existing methods for identification based on traditional machine learning and neural networks are limited by high model training costs and low recognition accuracy. To address these problems, we proposed a YOLOv7 maize pests identification method incorporating the Adan optimizer. First, we selected three major corn pests, corn borer, armyworm and bollworm as research objects. Then, we collected and constructed a corn pests dataset by using data augmentation to address the problem of scarce corn pests data. Second, we chose the YOLOv7 network as the detection model, and we proposed to replace the original optimizer of YOLOv7 with the Adan optimizer for its high computational cost. The Adan optimizer can efficiently sense the surrounding gradient information in advance, allowing the model to escape sharp local minima. Thus, the robustness and accuracy of the model can be improved while significantly reducing the computing power. Finally, we did ablation experiments and compared the experiments with traditional methods and other common object detection networks. Theoretical analysis and experimental result show that the model incorporating with Adan optimizer only requires 1/2-2/3 of the computing power of the original network to obtain performance beyond that of the original network. The mAP@[.5:.95] (mean Average Precision) of the improved network reaches 96.69% and the precision reaches 99.95%. Meanwhile, the mAP@[.5:.95] was improved by 2.79%-11.83% compared to the original YOLOv7 and 41.98%-60.61% compared to other common object detection models. In complex natural scenes, our proposed method is not only time-efficient and has higher recognition accuracy, reaching the level of SOTA.
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spelling pubmed-102776782023-06-20 A YOLOv7 incorporating the Adan optimizer based corn pests identification method Zhang, Chong Hu, Zhuhua Xu, Lewei Zhao, Yaochi Front Plant Sci Plant Science Major pests of corn insects include corn borer, armyworm, bollworm, aphid, and corn leaf mites. Timely and accurate detection of these pests is crucial for effective pests control and scientific decision making. However, existing methods for identification based on traditional machine learning and neural networks are limited by high model training costs and low recognition accuracy. To address these problems, we proposed a YOLOv7 maize pests identification method incorporating the Adan optimizer. First, we selected three major corn pests, corn borer, armyworm and bollworm as research objects. Then, we collected and constructed a corn pests dataset by using data augmentation to address the problem of scarce corn pests data. Second, we chose the YOLOv7 network as the detection model, and we proposed to replace the original optimizer of YOLOv7 with the Adan optimizer for its high computational cost. The Adan optimizer can efficiently sense the surrounding gradient information in advance, allowing the model to escape sharp local minima. Thus, the robustness and accuracy of the model can be improved while significantly reducing the computing power. Finally, we did ablation experiments and compared the experiments with traditional methods and other common object detection networks. Theoretical analysis and experimental result show that the model incorporating with Adan optimizer only requires 1/2-2/3 of the computing power of the original network to obtain performance beyond that of the original network. The mAP@[.5:.95] (mean Average Precision) of the improved network reaches 96.69% and the precision reaches 99.95%. Meanwhile, the mAP@[.5:.95] was improved by 2.79%-11.83% compared to the original YOLOv7 and 41.98%-60.61% compared to other common object detection models. In complex natural scenes, our proposed method is not only time-efficient and has higher recognition accuracy, reaching the level of SOTA. Frontiers Media S.A. 2023-06-05 /pmc/articles/PMC10277678/ /pubmed/37342143 http://dx.doi.org/10.3389/fpls.2023.1174556 Text en Copyright © 2023 Zhang, Hu, Xu and Zhao 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
Zhang, Chong
Hu, Zhuhua
Xu, Lewei
Zhao, Yaochi
A YOLOv7 incorporating the Adan optimizer based corn pests identification method
title A YOLOv7 incorporating the Adan optimizer based corn pests identification method
title_full A YOLOv7 incorporating the Adan optimizer based corn pests identification method
title_fullStr A YOLOv7 incorporating the Adan optimizer based corn pests identification method
title_full_unstemmed A YOLOv7 incorporating the Adan optimizer based corn pests identification method
title_short A YOLOv7 incorporating the Adan optimizer based corn pests identification method
title_sort yolov7 incorporating the adan optimizer based corn pests identification method
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10277678/
https://www.ncbi.nlm.nih.gov/pubmed/37342143
http://dx.doi.org/10.3389/fpls.2023.1174556
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