Abstract
AI-based pest detection is gaining popularity in data-centric scenarios, providing farmers with excellent performance and decision support for pest control. However, these approaches often face challenges that require complex architectures. Alternatively, data-centric approaches aim to enhance the quality of training data. In this study, we present an approach that is particularly relevant when dealing with low data. Our proposed approach improves annotation quality without requiring additional manpower. We trained a model with data of inferior annotation quality and utilized its predictions to generate new annotations of higher quality. Results from our study demonstrate that, using a small dataset of 200 images with low resolution and variable lighting conditions, our model can improve the mean average precision (mAP) score by 1.1 points.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
Open-source online tool: https://github.com/heartexlabs/labelImg.
References
Bernhard, M., Schubert, M.: Correcting imprecise object locations for training object detectors in remote sensing applications. Remote Sens. 13(24) (2021). https://doi.org/10.3390/rs13244962, www.mdpi.com/2072-4292/13/24/4962
Cardoso, B., Silva, C., Costa, J., Ribeiro, B.: Internet of things meets computer vision to make an intelligent pest monitoring network. Appl. Sci. 12(18) (2022). https://doi.org/10.3390/app12189397, www.mdpi.com/2076-3417/12/18/9397
Costa, D., Silva, C., Costa, J., Ribeiro, B.: Optimizing object detection models via active learning. In: Iberian Conference on Pattern Recognition and Image Analysis: Pattern Recognition and Image Analysis. Springer International Publishing (2023)
Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics (2023). github.com/ultralytics/ultralytics
Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., NanoCode012, Kwon, Y., Michael, K., TaoXie, Fang, J., imyhxy, Lorna, Yifu, Wong, C.V.A., Montes, D., Wang, Z., Fati, C., Nadar, J., Laughing, UnglvKitDe, Sonck, V., tkianai, yxNONG, Skalski, P., Hogan, A., Nair, D., Strobel, M., Jain, M.: ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation (2022). https://doi.org/10.5281/zenodo.7347926, 10.5281/zenodo.7347926
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., Duerig, T., Ferrari, V.: The open images dataset v4. Int. J. Comput. Vis. 128(7), 1956–1981 (2020). https://doi.org/10.1007/s11263-020-01316-z, 10.1007/2Fs11263-020-01316-z
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision - ECCV 2014, pp. 740–755. Springer International Publishing, Cham (2014)
Ma, J., Ushiku, Y., Sagara, M.: The effect of improving annotation quality on object detection datasets: a preliminary study. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 4849–4858 (2022). https://doi.org/10.1109/CVPRW56347.2022.00532
Nieuwenhuizen, A., Hemming, J., Suh, H.: Detection and classification of insects on stick-traps in a tomato crop using faster r-cnn (2018). nccv18.nl/program/; the Netherlands Conference on Computer Vision, NCCV18; Conference date: 26-09-2018 Through 27-09-2018
Northcutt, C.G., Athalye, A., Mueller, J.: Pervasive label errors in test sets destabilize machine learning benchmarks (2021)
Ren, P., Xiao, Y., Chang, X., Huang, P.Y., Li, Z., Gupta, B.B., Chen, X., Wang, X.: A survey of deep active learning (2021)
Sani, I., Ismail, S.I., Abdullah, S., Jalinas, J., Jamian, S., Saad, N.: A review of the biology and control of whitefly, bemisia tabaci (hemiptera: Aleyrodidae), with special reference to biological control using entomopathogenic fungi. Insects 11(9) (2020). https://doi.org/10.3390/insects11090619, www.mdpi.com/2075-4450/11/9/619
Terven, J., Cordova-Esparza, D.M.: A comprehensive review of yolo: from yolov1 to yolov8 and beyond (2023)
Wang, S., Gao, J., Li, B., Hu, W.: Narrowing the gap: improved detector training with noisy location annotations. Trans. Img. Proc. 31, 6369–6380 (2022). https://doi.org/10.1109/TIP.2022.3211468
Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., He, L.: A survey of human-in-the-loop for machine learning. Futur. Gener. Comput. Syst. 135, 364–381 (2022) https://doi.org/10.1016/j.future.2022.05.014, www.sciencedirect.com/science/article/pii/S0167739X22001790
Zha, D., Bhat, Z.P., Lai, K.H., Yang, F., Jiang, Z., Zhong, S., Hu, X.: Data-centric artificial intelligence: a survey (2023)
Acknowledgments
This work was supported by project PEGADA 4.0 (PRR-C05-i03-000099), financed by the PPR - Plano de Recuperação e Resiliência and by national funds through FCT, within the scope of the project CISUC (UID/CEC/00326/2020).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Costa, D., Silva, C., Costa, J., Ribeiro, B. (2023). Enhancing Pest Detection Models Through Improved Annotations. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14116. Springer, Cham. https://doi.org/10.1007/978-3-031-49011-8_29
Download citation
DOI: https://doi.org/10.1007/978-3-031-49011-8_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49010-1
Online ISBN: 978-3-031-49011-8
eBook Packages: Computer ScienceComputer Science (R0)