*Result*: Preventing exotic pet beetle invasion with an improved lightweight and efficient pest detection model deployed on mobile devices.
Title:
Preventing exotic pet beetle invasion with an improved lightweight and efficient pest detection model deployed on mobile devices.
Authors:
Bi H; State Key Laboratory to Efficient Production of Forest Resources, Beijing Forestry University, Beijing, China., Liu J; Plant Quarantine Department, Technical Center for Animal, Plant and Food Inspection and Quarantine of Shanghai Customs, Shanghai, China., Zong S; State Key Laboratory to Efficient Production of Forest Resources, Beijing Forestry University, Beijing, China., Chan X; Customs Inspection and Quarantine Department, Shanghai Customs College, Shanghai, China., Xin X; State Key Laboratory to Efficient Production of Forest Resources, Beijing Forestry University, Beijing, China., Yang Y; Customs Inspection and Quarantine Department, Shanghai Customs College, Shanghai, China.
Source:
Pest management science [Pest Manag Sci] 2026 Feb; Vol. 82 (2), pp. 1748-1764. Date of Electronic Publication: 2025 Oct 28.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Published for SCI by Wiley Country of Publication: England NLM ID: 100898744 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1526-4998 (Electronic) Linking ISSN: 1526498X NLM ISO Abbreviation: Pest Manag Sci Subsets: MEDLINE
Imprint Name(s):
Original Publication: West Sussex, UK : Published for SCI by Wiley, c2000-
MeSH Terms:
References:
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Hulme PE, Bacher S, Kenis M, Klotz S, Kühn I, Minchin D et al., Grasping at the routes of biological invasions: a framework for integrating pathways into policy. J Appl Ecol 45:403–414 (2008). https://doi.org/10.1111/j.1365-2664.2007.01442.x.
Collard R‐C, Animal Traffic: Lively Capital in the Global Exotic pet Trade. Duke University Press, Durham, NC (2020).
Mohemed Osman SE, Hamid HA, Ahmed Ishag AES and Salim Eisa MA, Survey And Level Of Infestation Of pachnoda interrupta (oliver) (coleoptera: scarabaeidae) In Gedarif State. Int J Eng Appl Sci Tech 4:73–77 (2020). https://doi.org/10.33564/IJEAST.2020.v04i11.015.
Fu C, Qian Q, Deng X, Zhuo Z and Xu D, Prediction and analysis of the global suitable habitat of the oryctes rhinoceros (linnaeus, 1758) (coleoptera: scarabaeidae) based on the MaxEnt model. Insects 15:774 (2024). https://doi.org/10.3390/insects15100774.
Okabe K and Goka K, Potential impacts on Japanese fauna of canestriniid mites (Acari: Astigmata) accidentally introduced with pet lucanid beetles from Southeast Asia. Biodivers Conserv 17:71–81 (2008). https://doi.org/10.1007/s10531-007-9231-1.
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Venette RC, Gordon DR, Juzwik J, Koch FH, Liebhold AM, Peterson RKD et al., Early intervention strategies for invasive species management: connections between risk assessment, prevention efforts, eradication, and other rapid responses, in Invasive Species in Forests and Rangelands of the United States: A Comprehensive Science Synthesis for the United States Forest Sector, ed. by Poland TM, Patel‐Weynand T, Finch DM, Miniat CF, Hayes DC and Lopez VM. Springer International Publishing, Cham, pp. 111–131 (2021).
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Jiao L, Dong S, Zhang S, Xie C and Wang H, AF‐RCNN: an anchor‐free convolutional neural network for multi‐categories agricultural pest detection. Comput Electron Agric 174:105522 (2020). https://doi.org/10.1016/j.compag.2020.105522.
Li W, Wang D, Li M, Gao Y, Wu J and Yang X, Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse. Comput Electron Agric 183:106048 (2021). https://doi.org/10.1016/j.compag.2021.106048.
Wang F, Wang R, Xie C, Zhang J, Li R and Liu L, Convolutional neural network based automatic pest monitoring system using hand‐held mobile image analysis towards non‐site‐specific wild environment. Comput Electron Agric 187:106268 (2021). https://doi.org/10.1016/j.compag.2021.106268.
Jiao L, Xie C, Chen P, Du J, Li R and Zhang J, Adaptive feature fusion pyramid network for multi‐classes agricultural pest detection. Comput Electron Agric 195:106827 (2022). https://doi.org/10.1016/j.compag.2022.106827.
Wang R, Jiao L, Xie C, Chen P, Du J and Li R, S‐RPN: sampling‐balanced region proposal network for small crop pest detection. Comput Electron Agric 187:106290 (2021). https://doi.org/10.1016/j.compag.2021.106290.
Liu L, Wang R, Xie C, Yang P, Wang F, Sudirman S et al., PestNet: an end‐to‐end deep learning approach for large‐scale multi‐class Pest detection and classification. IEEE Access 7:45301–45312 (2019). https://doi.org/10.1109/ACCESS.2019.2909522.
Dong S, Wang R, Liu K, Jiao L, Li R, Du J et al., CRA‐net: a channel recalibration feature pyramid network for detecting small pests. Comput Electron Agric 191:106518 (2021). https://doi.org/10.1016/j.compag.2021.106518.
Xu L, Shi X, Tang Z, He Y, Yang N, Ma W et al., ASFL‐YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family. Front Plant Sci 14:1176300 (2023). https://doi.org/10.3389/fpls.2023.1176300.
Yu Y, Zhou Q, Wang H, Lv K, Zhang L, Li J et al., LP‐YOLO: a lightweight object detection network regarding insect pests for Mobile terminal devices based on improved YOLOv8. Agri 14:1420 (2024). https://doi.org/10.3390/agriculture14081420.
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Zhang X, Liang K and Zhang Y, Plant pest and disease lightweight identification model by fusing tensor features and knowledge distillation. Front Plant Sci 15:1443815 (2024). https://doi.org/10.3389/fpls.2024.1443815.
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Wang C‐Y, Bochkovskiy A and Liao H‐YM, YOLOv7: Trainable bag‐of‐freebies sets new state‐of‐the‐art for real‐time object detectors (2022). https://doi.org/10.48550/arXiv.2207.02696.
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Zhao Y, Lv W, Xu S, Wei J, Wang G, Dang Q et al., DETRs beat YOLOs on real‐time object detection. Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Seattle, WA, USA. Piscataway, NJ (2024). https://doi.org/10.1109/CVPR52733.2024.01605.
Huang S, Lu Z, Cun X, Yu Y, Zhou X and Shen X, DEIM: DETR with Improved Matching for Fast Convergence. Proceedings of the 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEEE Nashville, TN, USA. Piscataway, NJ, (2025). https://doi.org/10.1109/CVPR52734.2025.01412.
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Venette RC and Koch RL, IPM for invasive species, in Integrated Pest Management, ed. by Radcliffe EB, Hutchison WD and Cancelado RE. Cambridge University Press, Cambridge, pp. 424–436 (2009).
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Lodge DM, Williams S, MacIsaac HJ, Hayes KR, Leung B, Reichard S et al., Biological invasions: recommendations for U.S. policy and management. Ecol Appl 16:2035–2054 (2006). https://doi.org/10.1890/1051-0761(2006)016[2035:BIRFUP]2.0.CO;2.
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Sun X, Wang P, Wang C, Liu Y and Fu K, PBNet: part‐based convolutional neural network for complex composite object detection in remote sensing imagery. ISPRS J Photogramm Remote Sens 173:50–65 (2021). https://doi.org/10.1016/j.isprsjprs.2020.12.015.
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Jenkins PT, Free trade and exotic species introductions. Conserv Biol 10:300–302 (1996). https://doi.org/10.1046/j.1523-1739.1996.10010300.x.
Mack RN, Simberloff D, Mark Lonsdale W, Evans H, Clout M and Bazzaz FA, Biotic invasions: causes, epidemiology, global consequences, and control. Ecol Appl 10:689–710 (2000). https://doi.org/10.1890/1051-0761(2000)010[0689:BICEGC]2.0.CO;2.
Bradshaw CJA, Leroy B, Bellard C, Roiz D, Albert C, Fournier A et al., Massive yet grossly underestimated global costs of invasive insects. Nat Commun 7:12986 (2016). https://doi.org/10.1038/ncomms12986.
Hulme PE, Bacher S, Kenis M, Klotz S, Kühn I, Minchin D et al., Grasping at the routes of biological invasions: a framework for integrating pathways into policy. J Appl Ecol 45:403–414 (2008). https://doi.org/10.1111/j.1365-2664.2007.01442.x.
Collard R‐C, Animal Traffic: Lively Capital in the Global Exotic pet Trade. Duke University Press, Durham, NC (2020).
Mohemed Osman SE, Hamid HA, Ahmed Ishag AES and Salim Eisa MA, Survey And Level Of Infestation Of pachnoda interrupta (oliver) (coleoptera: scarabaeidae) In Gedarif State. Int J Eng Appl Sci Tech 4:73–77 (2020). https://doi.org/10.33564/IJEAST.2020.v04i11.015.
Fu C, Qian Q, Deng X, Zhuo Z and Xu D, Prediction and analysis of the global suitable habitat of the oryctes rhinoceros (linnaeus, 1758) (coleoptera: scarabaeidae) based on the MaxEnt model. Insects 15:774 (2024). https://doi.org/10.3390/insects15100774.
Okabe K and Goka K, Potential impacts on Japanese fauna of canestriniid mites (Acari: Astigmata) accidentally introduced with pet lucanid beetles from Southeast Asia. Biodivers Conserv 17:71–81 (2008). https://doi.org/10.1007/s10531-007-9231-1.
Early R, Bradley BA, Dukes JS, Lawler JJ, Olden JD, Blumenthal DM et al., Global threats from invasive alien species in the twenty‐first century and national response capacities. Nat Commun 7:12485 (2016). https://doi.org/10.1038/ncomms12485.
Epanchin‐Niell RS and Liebhold AM, Benefits of invasion prevention: effect of time lags, spread rates, and damage persistence. Ecol Econ 116:146–153 (2015). https://doi.org/10.1016/j.ecolecon.2015.04.014.
Venette RC, Gordon DR, Juzwik J, Koch FH, Liebhold AM, Peterson RKD et al., Early intervention strategies for invasive species management: connections between risk assessment, prevention efforts, eradication, and other rapid responses, in Invasive Species in Forests and Rangelands of the United States: A Comprehensive Science Synthesis for the United States Forest Sector, ed. by Poland TM, Patel‐Weynand T, Finch DM, Miniat CF, Hayes DC and Lopez VM. Springer International Publishing, Cham, pp. 111–131 (2021).
Zhong Y, Chen Z, Du Z, Chen Y, Huang Y and Pan B, Identification techniques of endangered species and their application fields. China Port Sci Technol 4:39–44 (2022).
Kress WJ and Erickson DL, DNA barcodes: methods and protocols. Methods Mol Biol Clifton NJ 858:3–8 (2012). https://doi.org/10.1007/978-1-61779-591-6_1.
Wahlberg N and Wheat CW, Genomic outposts serve the phylogenomic pioneers: designing novel nuclear markers for genomic DNA extractions of lepidoptera. Syst Biol 57:231–242 (2008). https://doi.org/10.1080/10635150802033006.
Chen M, Chen L, Yi T, Zhang R, Xia L, Qu C et al., Development of a low‐power automatic monitoring system for spodoptera frugiperda (J. E. Smith). Agri 13:843 (2023). https://doi.org/10.3390/agriculture13040843.
Jiao L, Dong S, Zhang S, Xie C and Wang H, AF‐RCNN: an anchor‐free convolutional neural network for multi‐categories agricultural pest detection. Comput Electron Agric 174:105522 (2020). https://doi.org/10.1016/j.compag.2020.105522.
Li W, Wang D, Li M, Gao Y, Wu J and Yang X, Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse. Comput Electron Agric 183:106048 (2021). https://doi.org/10.1016/j.compag.2021.106048.
Wang F, Wang R, Xie C, Zhang J, Li R and Liu L, Convolutional neural network based automatic pest monitoring system using hand‐held mobile image analysis towards non‐site‐specific wild environment. Comput Electron Agric 187:106268 (2021). https://doi.org/10.1016/j.compag.2021.106268.
Jiao L, Xie C, Chen P, Du J, Li R and Zhang J, Adaptive feature fusion pyramid network for multi‐classes agricultural pest detection. Comput Electron Agric 195:106827 (2022). https://doi.org/10.1016/j.compag.2022.106827.
Wang R, Jiao L, Xie C, Chen P, Du J and Li R, S‐RPN: sampling‐balanced region proposal network for small crop pest detection. Comput Electron Agric 187:106290 (2021). https://doi.org/10.1016/j.compag.2021.106290.
Liu L, Wang R, Xie C, Yang P, Wang F, Sudirman S et al., PestNet: an end‐to‐end deep learning approach for large‐scale multi‐class Pest detection and classification. IEEE Access 7:45301–45312 (2019). https://doi.org/10.1109/ACCESS.2019.2909522.
Dong S, Wang R, Liu K, Jiao L, Li R, Du J et al., CRA‐net: a channel recalibration feature pyramid network for detecting small pests. Comput Electron Agric 191:106518 (2021). https://doi.org/10.1016/j.compag.2021.106518.
Xu L, Shi X, Tang Z, He Y, Yang N, Ma W et al., ASFL‐YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family. Front Plant Sci 14:1176300 (2023). https://doi.org/10.3389/fpls.2023.1176300.
Yu Y, Zhou Q, Wang H, Lv K, Zhang L, Li J et al., LP‐YOLO: a lightweight object detection network regarding insect pests for Mobile terminal devices based on improved YOLOv8. Agri 14:1420 (2024). https://doi.org/10.3390/agriculture14081420.
Kang H, Ai L, Zhen Z, Lu B, Man Z, Yi P et al., A novel deep learning model for accurate Pest detection and edge computing deployment. Insects 14:660 (2023). https://doi.org/10.3390/insects14070660.
Zhang X, Liang K and Zhang Y, Plant pest and disease lightweight identification model by fusing tensor features and knowledge distillation. Front Plant Sci 15:1443815 (2024). https://doi.org/10.3389/fpls.2024.1443815.
Khanam R and Hussain M, What is YOLOv5: a deep look into the internal features of the popular object detector (2024a). https://doi.org/10.48550/arXiv.2407.20892.
Li C, Li L, Jiang H, Weng K, Geng Y, Li L et al., YOLOv6: a single‐stage object detection framework for industrial applications (2022). https://doi.org/10.48550/arXiv.2209.02976.
Wang C‐Y, Bochkovskiy A and Liao H‐YM, YOLOv7: Trainable bag‐of‐freebies sets new state‐of‐the‐art for real‐time object detectors (2022). https://doi.org/10.48550/arXiv.2207.02696.
Yaseen M, What is YOLOv8: an in‐depth exploration of the internal features of the next‐generation object detector (2024). https://doi.org/10.48550/arXiv.2408.15857.
Wang CY, Yeh IH and Liao HYM, YOLOv9: learning what you want to learn using programmable gradient information (2024). https://doi.org/10.48550/arXiv.2402.13616.
Wang A, Chen H, Liu L, Chen K, Lin Z, Han J et al., YOLOv10: real‐time end‐to‐end object detection (2024). https://doi.org/10.48550/arXiv.2405.14458.
Khanam R and Hussain M, YOLOv11: an overview of the key architectural enhancements (2024b). https://doi.org/10.48550/arXiv.2410.17725.
Yu Z, Huang H, Chen W, Su Y, Liu Y and Wang X, YOLO‐FaceV2: a scale and occlusion aware face detector (2022). https://doi.org/10.48550/arXiv.2208.02019.
Lee J, Park S, Mo S, Ahn S and Shin J, Layer‐adaptive sparsity for the magnitude‐based pruning (2021). https://doi.org/10.48550/arXiv.2010.07611.
Hinton G, Vinyals O and Dean J, Distilling the knowledge in a neural network. Comput Therm Sci 14:38–39 (2015). https://doi.org/10.48550/arXiv.1503.02531.
Wang Q, Liu L, Yu W, Chen S, Gong J and Chen P, BCKD: Block‐Correlation Knowledge Distillation. Proceedings of the 2023 IEEE International Conference on Image Processing (ICIP). IEEE, Kuala Lumpur, Malaysia. Piscataway, NJ, (2023). https://doi.org/10.1109/ICIP49359.2023.10222195.
Ren S, He K, Girshick R and Sun J, Faster R‐CNN: towards real‐time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137–1149 (2017). https://doi.org/10.1109/TPAMI.2016.2577031.
Cai Z and Vasconcelos N, Cascade R‐CNN: Delving into High Quality Object Detection. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Salt Lake City, UT, USA. Piscataway, NJ (2018). https://doi.org/10.1109/CVPR.2018.00644.
Zhao Y, Lv W, Xu S, Wei J, Wang G, Dang Q et al., DETRs beat YOLOs on real‐time object detection. Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Seattle, WA, USA. Piscataway, NJ (2024). https://doi.org/10.1109/CVPR52733.2024.01605.
Huang S, Lu Z, Cun X, Yu Y, Zhou X and Shen X, DEIM: DETR with Improved Matching for Fast Convergence. Proceedings of the 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEEE Nashville, TN, USA. Piscataway, NJ, (2025). https://doi.org/10.1109/CVPR52734.2025.01412.
Westbrooks RG, New approaches for early detection and rapid response to invasive plants in the United States. Weed Technol 18:1468–1471 (2004). https://doi.org/10.1614/0890-037X(2004)018[1468:NAFEDA]2.0.CO;2.
Venette RC and Koch RL, IPM for invasive species, in Integrated Pest Management, ed. by Radcliffe EB, Hutchison WD and Cancelado RE. Cambridge University Press, Cambridge, pp. 424–436 (2009).
Leung B, Lodge DM, Finnoff D, Shogren JF, Lewis MA and Lamberti G, An ounce of prevention or a pound of cure: bioeconomic risk analysis of invasive species. Proc Biol Sci 269:2407–2413 (2002). https://doi.org/10.1098/rspb.2002.2179.
Lodge DM, Williams S, MacIsaac HJ, Hayes KR, Leung B, Reichard S et al., Biological invasions: recommendations for U.S. policy and management. Ecol Appl 16:2035–2054 (2006). https://doi.org/10.1890/1051-0761(2006)016[2035:BIRFUP]2.0.CO;2.
Bi H, Li T, Xin X, Shi H, Li L and Zong S, Non‐destructive estimation of wood‐boring pest density in living trees using X‐ray imaging and edge computing techniques. Comput Electron Agric 233:110183 (2025). https://doi.org/10.1016/j.compag.2025.110183.
Lin Y, Huang Z, Liang Y, Liu Y and Jiang W, AG‐YOLO: a rapid citrus fruit detection algorithm with global context fusion. Agri 14:114 (2024). https://doi.org/10.3390/agriculture14010114.
Lu J, Chen P, Yu C, Lan Y, Yu L, Yang R et al., Lightweight green citrus fruit detection method for practical environmental applications. Comput Electron Agric 215:108205 (2023). https://doi.org/10.1016/j.compag.2023.108205.
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Grant Information:
2315020A2020 Shanghai Customs College Scientific Research Startup Funding Project; 3202088A2024 Shanghai Key Curriculum Construction Project
Contributed Indexing:
Keywords: deep learning; knowledge distillation; mobile deployment; model pruning; module design; pest detection
Entry Date(s):
Date Created: 20251028 Date Completed: 20260111 Latest Revision: 20260111
Update Code:
20260130
DOI:
10.1002/ps.70322
PMID:
41147760
Database:
MEDLINE
*Further Information*
*Background: The illegal smuggling of exotic pet beetles presents a growing threat to global ecosystems. Customs authorities play a critical role in preventing biological invasions, yet current identification methods rely heavily on expert knowledge and time-consuming laboratory analysis, which limits rapid responses at ports of entry. To address this issue, we propose EPB-YOLO-PD, a lightweight, mobile-deployable detection model for real-time recognition of exotic pet beetles. The source code is available at https://github.com/bihaojie/EPB-YOLO-PD.
Results: EPB-YOLO-PD incorporates three originally designed components - the Feature Aggregation and Mixing Network (FAMNet), Multi-Scale Efficient Lightweight Optimization Network (MELON), and Partial Multi-Head Self-Attention Residual Block (C4PMS) - along with an improved detection head (CAHead), and a newly introduced loss function (Slide Loss). Structural pruning and knowledge distillation are applied to reduce model size and improve inference speed. When tested on a custom dataset of 13 intercepted species, the model achieved detection accuracies between 93.3% and 99.3%. Compared to the YOLOv11n baseline, EPB-YOLO-PD demonstrated a 2.0% increase in mAP0.5 (97.3%), a 74.04% reduction in model size (1.35 MB), and a 65.08% decrease in computational complexity (2.2 GFLOPs). The PetBeetle Finder app, based on this model, runs at over 25 frames per second (FPS) on a Huawei Mate 40 smartphone.
Conclusions: EPB-YOLO-PD offers an effective solution for real-time detection of exotic pet beetles at customs checkpoints. It enables rapid and accurate classification, effectively handling challenging scenarios such as incomplete morphological features and visually confusing backgrounds, and provides a replicable framework for intercepting other invasive species. © 2025 Society of Chemical Industry.
(© 2025 Society of Chemical Industry.)*