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Effective Insect Recognition Based on Deep Neural Network Models in Complex Background

Published: 26 August 2021 Publication History

Abstract

In biology, insects have the most species, number, distribution, and adaptability. Insect recognition is the basis of insect research and pest control. However, current insect recognition work mainly relies on few insect taxonomy experts. With the rapid development of computer technology, we can employ the computer instead of experts to distinguish insects accurately. To recognize insects effectively, especially the subtle differences between subcategories, we combined FGVC (Fine-Grained Visual Categorization) with deep learning, and applied Inception V3, VGG16_bn, and ResNet50 in the research of insect recognition and classification. In this paper, the experimental results showed that all of the three methods had high accuracy, the Inception V3 reached 98.69%, the VGG16_bn reached 97.80% and ResNet50 reached 97.94%. We also used label smoothing technology to reduce the errors caused by label errors and improve the accuracy of different models.

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  • (2024)Artificial neuronal networks are revolutionizing entomological researchJournal of Applied Entomology10.1111/jen.13227148:2(232-251)Online publication date: 10-Jan-2024
  • (2024)Analyzing Real-Time Insect Detection in Smart Connected FarmsComputer10.1109/MC.2023.333525457:12(38-46)Online publication date: 1-Dec-2024
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cover image ACM Other conferences
HP3C '21: Proceedings of the 5th International Conference on High Performance Compilation, Computing and Communications
June 2021
71 pages
ISBN:9781450389648
DOI:10.1145/3471274
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 August 2021

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Author Tags

  1. Inception V3
  2. ResNet50
  3. VGG16_bn
  4. deep learning
  5. insect recognition

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • Chengdu Science and Technology Project
  • Natural Science Foundation of China
  • The 2020 Special funds for forestry industry development

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HP3C'21

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Cited By

View all
  • (2024)ARTIFICIAL INTELLIGENCE AND ITS TOOLS IN PEST CONTROL FOR AGRICULTURAL PRODUCTION: A REVIEWINTELIGENCIA ARTIFICIAL Y SUS HERRAMIENTAS EN EL CONTROL DE PLAGAS PARA LA PRODUCCIÓN AGRÍCOLA: UNA REVISIÓNARTIFICIAL INTELLIGENCE AND ITS TOOLS IN PEST CONTROL FOR AGRICULTURAL PRODUCTION: A REVIEWINTELIGÊNCIA ARTIFICIAL E SUAS FERRAMENTAS NO CONTROLE DE PRAGAS PARA PRODUÇÃO AGRÍCOLA: UMA REVISÃORECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-621810.47820/recima21.v5i5.52775:5(e555277)Online publication date: 27-May-2024
  • (2024)Artificial neuronal networks are revolutionizing entomological researchJournal of Applied Entomology10.1111/jen.13227148:2(232-251)Online publication date: 10-Jan-2024
  • (2024)Analyzing Real-Time Insect Detection in Smart Connected FarmsComputer10.1109/MC.2023.333525457:12(38-46)Online publication date: 1-Dec-2024
  • (2023)Multi-class Classification of Insects using Deep Neural Networks2023 International Conference on Computer Communication and Informatics (ICCCI)10.1109/ICCCI56745.2023.10128549(1-5)Online publication date: 23-Jan-2023
  • (2022)Next generation insect taxonomic classification by comparing different deep learning algorithmsPLOS ONE10.1371/journal.pone.027909417:12(e0279094)Online publication date: 30-Dec-2022

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