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

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

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
Open this paper in CoChat — free accountOpen PDF

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.

Overview

Integrity signals

Registry

Crossref · 10.3390/f14071484

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 2 references

Research works printed but not linked: 33

  1. Hurley, 2012, Perception and knowledge of the Sirex woodwasp and other forest pests in South Africa, Agric. For. Entomol., 14, 306, 10.1111/j.1461-9563.2012.00570.x · DOI
  2. Hiary, 2011, Fast and Accurate Detection and Classification of Plant Diseases, Int. J. Comput. Appl., 17, 31
  3. Ebrahimi, 2017, Vision-based pest detection based on SVM classification method, Comput. Electron. Agric., 137, 52, 10.1016/j.compag.2017.03.016 · DOI
  4. Zayas, 1998, Detection of insects in bulk wheat samples with machine vision, Trans. Am. Soc. Agric. Eng., 41, 883, 10.13031/2013.17206 · DOI
  5. Li, 2015, Detection of small-sized insect pest in greenhouses based on multifractal analysis, Opt.-Int. J. Light Electron Opt., 126, 2138, 10.1016/j.ijleo.2015.05.096 · DOI
  6. Wang, 2012, A new automatic identification system of insect images at the order level, Knowl.-Based Syst., 33, 102, 10.1016/j.knosys.2012.03.014 · DOI
  7. Liu, 2016, Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network, Sci. Rep., 6, 20410, 10.1038/srep20410 · DOI
  8. Zhu, 2021, Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning, Front. Plant Sci., 12, 695749, 10.3389/fpls.2021.695749 · DOI
  9. Xia, 2015, Automatic identification and counting of small size pests in greenhouse conditions with low computational cost, Ecol. Inform., 29, 139, 10.1016/j.ecoinf.2014.09.006 · DOI
  10. Girshick, R. (2015, January 7–13). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile. · DOI · Paper page
  11. Cai, Z., and Vasconcelos, N. (2018, January 18–23). Cascade R-CNN: Delving into High Quality Object Detection. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA. · DOI · Paper page
  12. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11–14). SSD: Single Shot MultiBox Detector. Proceedings of the Computer Vision—ECCV 2016, Amsterdam, The Netherlands. · DOI · Paper page
  13. Chen, Q., Wang, Y., Yang, T., Zhang, X., Cheng, J., and Sun, J. (2021, January 20–25). You Only Look One-level Feature. Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA. · DOI
  14. Roy, 2022, Real-time growth stage detection model for high degree of occultation using DenseNet-fused YOLOv4, Comput. Electron. Agric., 193, 106694, 10.1016/j.compag.2022.106694 · DOI
  15. Lawal, 2021, Tomato detection based on modified YOLOv3 framework, Sci. Rep., 11, 1447, 10.1038/s41598-021-81216-5 · DOI
  16. Li, D., Wang, R., Xie, C., Liu, L., Zhang, J., Li, R., Wang, F., Zhou, M., and Liu, W. (2020). A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network. Sensors, 20. · DOI
  17. Sun, 2018, Automatic in-trap pest detection using deep learning for pheromone-based Dendroctonus valens monitoring, Biosyst. Eng., 176, 140, 10.1016/j.biosystemseng.2018.10.012 · DOI
  18. Hong, S.-J., Kim, S.-Y., Kim, E., Lee, C.-H., Lee, J.-S., Lee, D.-S., Bang, J., and Kim, G. (2020). Moth Detection from Pheromone Trap Images Using Deep Learning Object Detectors. Agriculture, 10. · DOI
  19. Jiao, 2022, Global Context-Aware-Based Deformable Residual Network Module for Precise Pest Recognition and Detection, Front. Plant Sci., 13, 895944, 10.3389/fpls.2022.895944 · DOI
  20. Liu, 2022, A Dataset for Forestry Pest Identification, Front. Plant Sci., 13, 857104, 10.3389/fpls.2022.857104 · DOI
  21. Wu, X., Zhan, C., Lai, Y.K., Cheng, M.M., and Yang, J. (2019, January 15–20). IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition. Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA. · DOI
  22. Fu, L., Yang, Z., Wu, F., Zou, X., Lin, J., Cao, Y., and Duan, J. (2022). YOLO-Banana: A Lightweight Neural Network for Rapid Detection of Banana Bunches and Stalks in the Natural Environment. Agronomy, 12. · DOI
  23. Xiang, Q., Huang, X., Huang, Z., Chen, X., Cheng, J., and Tang, X. (2023). Yolo-Pest: An Insect Pest Object Detection Algorithm via CAC3 Module. Sensors, 23. · DOI
  24. Dai, X.Y., Chen, Y.P., Xiao, B., Chen, D.D., Liu, M.C., Yuan, L., Zhang, L., and Ieee Comp, S.O.C. (2021, January 20–25). Dynamic Head: Unifying Object Detection Heads with Attentions. Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021, Nashville, TN, USA. · DOI
  25. Zhuang, 2021, A Comprehensive Survey on Transfer Learning, Proc. IEEE, 109, 43, 10.1109/JPROC.2020.3004555 · DOI · Paper page
  26. Fraiwan, M., Faouri, E., and Khasawneh, N. (2022). Classification of Corn Diseases from Leaf Images Using Deep Transfer Learning. Plants, 11. · DOI
  27. Han, 2022, Advancing Radar Nowcasting through Deep Transfer Learning, IEEE Trans. Geosci. Remote Sens., 60, 1
  28. Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv.
  29. Ji, 2022, BiO(OH)xI1−x solid solution with rich oxygen vacancies: Interlayer guest hydroxyl for improved photocatalytic properties, J. Colloid Interface Sci., 605, 1, 10.1016/j.jcis.2021.07.060 · DOI
  30. Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv.
  31. Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv.
  32. Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.
  33. Khasawneh, N., Fraiwan, M., and Fraiwan, L. (2022). Detection of K-complexes in EEG signals using deep transfer learning and YOLOv3. Clust. Comput. · DOI

Cited by

Sorted by citations

See all 24

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Detection of Forestry Pests Based on Improved YOLOv5 and Transfer Learning. https://app.cochat.ai/papers/10.3390/f14071484
The paper
Liu, D., Lv, F., Guo, J., Zhang, H., & Zhu, L. (2023). Detection of Forestry Pests Based on Improved YOLOv5 and Transfer Learning. Forests. https://doi.org/10.3390/f14071484