Skip to main content

Investigating Generative Neural-Network Models for Building Pest Insect Detectors in Sticky Trap Images for the Peruvian Horticulture

  • Conference paper
  • First Online:
Information Management and Big Data (SIMBig 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1577))

Included in the following conference series:

Abstract

Pest insects are a problem in horticulture so their early detection is important for their control. Sticky traps are an inexpensive way to obtain insect samples, but manually identifying them is a time-consuming task. Building computational models to identify insect species in sticky trap images is therefore highly desirable. However, this is a challenging task due to the difficulty in getting sizeable sets of training images. In this paper, we studied the usefulness of three neural network generative models to synthesize pest insect images (DCGAN, WGAN, and VAE) for augmenting the training set and thus facilitate the induction of insect detector models. Experiments with images of seven species of pest insects of the Peruvian horticulture showed that the WGAN and VAE models are able to learn to generate images of such species. It was also found that the synthesized images can help to induce YOLOv5m detectors with significant gains in detection performance compared to not using synthesized data. A demo app that integrates the detector models can be accessed through the URL https://bit.ly/3uXW0Ee

Supported by Pontifical Catholic University of Peru.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 214–223. PMLR (2017)

    Google Scholar 

  2. Cañedo, V., Alfaro-Tapia, A., Kroschel, J.: Manejo Integrado de plagas de insectos en hortalizas Principios y referencias técnicas para la Sierra Central de Perú (2011)

    Google Scholar 

  3. Cho, J., Choi, J., Qiao, M., Kim, H., Uhm, K., Chon, T.S.: Automatic identification of whiteflies, aphids and thrips in greenhouse based on image analysis. Int. J. Math. Comput. Simul. 1, 46–53 (2007)

    Google Scholar 

  4. Espinoza, K., Valera, D.L., Torres, J.A., López, A., Molina-Aiz, F.D.: Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture. Comput. Electron. Agric. 127, 495–505 (2016)

    Article  Google Scholar 

  5. Goodfellow, I.J., et al.: Generative adversarial networks (2014)

    Google Scholar 

  6. Huang, J., Zeng, M., Li, W., Meng, X.: Application of data augmentation and migration learning in identification of diseases and pests in tea trees. In: 2019 Boston, Massachusetts July 7- July 10, 2019. American Society of Agricultural and Biological Engineers (2019)

    Google Scholar 

  7. Jocher, G., et al.: ultralytics/yolov5: v5.0 - YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations (2021)

    Google Scholar 

  8. Kingma, D.P., Welling, M.: Auto-encoding variational bayes (2014)

    Google Scholar 

  9. Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  10. Lu, C.Y., Arcega Rustia, D.J., Lin, T.T.: Generative adversarial network based image augmentation for insect pest classification enhancement. IFAC-PapersOnLine 52(30), 1–5 (2019)

    Article  MathSciNet  Google Scholar 

  11. van der Maaten, L., Hinton, G.: Viualizing data using t-SNE. J. Mach, Learn. Res. 9, 2579–2605 (2008)

    Google Scholar 

  12. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks (2016)

    Google Scholar 

  13. Rustia, D.J., Chao, J.J., Chung, J.Y., Lin, T.T.: An online unsupervised deep learning approach for an automated pest insect monitoring system. In: 2019 ASABE Annual International Meeting (2019)

    Google Scholar 

  14. Wang, C.Y., Liao, H.Y.M., Yeh, I.H., Wu, Y.H., Chen, P.Y., Hsieh, J.W.: CSPNet: a new backbone that can enhance learning capability of CNN (2019)

    Google Scholar 

  15. Wang, Q., Kulkarni, S.R., Verdu, S.: Divergence estimation for multidimensional densities via \(k\)-nearest-neighbor distances. IEEE Trans. Inf. Theory 55(5), 2392–2405 (2009)

    Article  MathSciNet  Google Scholar 

  16. Xia, C., Chon, T.S., Ren, Z., Lee, J.M.: Automatic identification and counting of small size pests in greenhouse conditions with low computational cost. Ecol. Inform. 29, 139–146 (2015)

    Article  Google Scholar 

  17. Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks (2019)

    Google Scholar 

  18. Zhong, Y., Gao, J., Lei, Q., Zhou, Y.: A vision-based counting and recognition system for flying insects in intelligent agriculture. Sensors 18, 1489 (2018)

    Article  Google Scholar 

  19. Zhou, H., Miao, H., Li, J., Jian, F., Jayas, D.S.: A low-resolution image restoration classifier network to identify stored-grain insects from images of sticky boards. Comput. Electron. Agric. 162, 593–601 (2019)

    Article  Google Scholar 

Download references

Acknowledgement

The authors gratefully acknowledge Artificial Intelligence Group of Pontifical Catholic University of Peru (IA–PUCP) for the support with the computational infrastructure for the experimental part of the present study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Joel Cabrera .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cabrera, J., Villanueva, E. (2022). Investigating Generative Neural-Network Models for Building Pest Insect Detectors in Sticky Trap Images for the Peruvian Horticulture. In: Lossio-Ventura, J.A., et al. Information Management and Big Data. SIMBig 2021. Communications in Computer and Information Science, vol 1577. Springer, Cham. https://doi.org/10.1007/978-3-031-04447-2_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-04447-2_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04446-5

  • Online ISBN: 978-3-031-04447-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics