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Cited by 24Smart Agriculture and AIAdvanced Neural Network ApplicationsIndustrial Vision Systems and Defect Detection

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Detection of Forestry Pests Based on Improved YOLOv5 and Transfer Learning

Open access
Liu, Dayang, Lv, Feng, Guo, Jingtao, Zhang, Huiting, Zhu, Liangkuan·2023·Forests
DOI 10.3390/f14071484
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Abstract

Infestations or parasitism by forestry pests can lead to adverse consequences for tree growth, development, and overall tree quality, ultimately resulting in ecological degradation. The identification and localization of forestry pests are of utmost importance for effective pest control within forest ecosystems. To tackle the challenges posed by variations in pest poses and similarities between different classes, this study introduced a novel end-to-end pest detection algorithm that leverages deep convolutional neural networks (CNNs) and a transfer learning technique. The basic architecture of the method is YOLOv5s, and the C2f module is adopted to replace part of the C3 module to obtain richer gradient information. In addition, the DyHead module is applied to improve the size, task, and spatial awareness of the model. To optimize network parameters and enhance pest detection ability, the model is initially trained using an agricultural pest dataset and subsequently fine-tuned with the forestry pest dataset. A comparative analysis was performed between the proposed method and other mainstream target detection approaches, including YOLOv4-Tiny, YOLOv6, YOLOv7, YOLOv8, and Faster RCNN. The experimental results demonstrated impressive performance in detecting 31 types of forestry pests, achieving a detection precision of 98.1%, recall of 97.5%, and mAP@.5:.95 of 88.1%. Significantly, our method outperforms all the compared target detection methods, showcasing a minimum improvement of 2.1% in mAP@.5:.95. The model has shown robustness and effectiveness in accurately detecting various pests.

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Metadata

Authors
Liu, D., Lv, F., Guo, J., Zhang, H., & Zhu, L.
Affiliations
Northeast Forestry University (CN)
Corresponding author
Zhu, Liangkuan
ORCID
Venue
Forests
Publisher
MDPI AG
Published
2023-07-20
Volume
14
Issue
7
Pages
1484
ISSN
1999-4907 (ISSN-L)
DOI
10.3390/f14071484
OpenAlex
W4384931321
Cited by
24
References
2
Citations by year
2026: 6 · 2025: 6 · 2024: 11 · 2023: 1
FWCI
3.79
Type
journal article
Language
en
Open access
Licence
Locations
Forests (publishedVersion, cc-by)
Funders
  • National Natural Science Foundation of China (32202147, 2021M690573, 2572020BF05)
  • China Postdoctoral Science Foundation (32202147, 2021M690573, 2572020BF05)
  • Fundamental Research Funds for the Central Universities (32202147, 2021M690573, 2572020BF05)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
Keywords
YOLOv5, transfer learning, object detection, C2f module, fine-tuning, forest pest management

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