Abstract
Weeds are a significant threat to agricultural production. Weed classification systems based on image analysis have offered innovative solutions to agricultural problems, with convolutional neural networks (CNNs) playing a pivotal role in this task. However, CNNs are limited in their ability to capture global relationships in images due to their localized convolutional operation. Vision Transformers (ViT) and Pyramid Vision Transformers (PVT) have emerged as viable solutions to overcome this limitation. Our study aims to determine the effectiveness of CNN, PVT, and ViT in classifying weeds in image datasets. We also examine if combining these methods in an ensemble can enhance classification performance. Our tests were conducted on significant agricultural datasets, including DeepWeeds and CottonWeedID15. The results indicate that a maximum of 3 methods in an ensemble, with only 15 epochs in training, can achieve high accuracy rates of up to 99.17%. This study demonstrates that high accuracies can be achieved with ease of implementation and only a few epochs.
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
References
Chen, D., Lu, Y., Li, Z., Young, S.: Performance evaluation of deep transfer learning on multi-class identification of common weed species in cotton production systems. Comput. Electron. Agric. 198, 107091 (2022)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Hasan, A.M., Sohel, F., Diepeveen, D., Laga, H., Jones, M.G.: A survey of deep learning techniques for weed detection from images. Comput. Electron. Agric. 184, 106067 (2021)
Hu, K., Coleman, G., Zeng, S., Wang, Z., Walsh, M.: Graph weeds net: a graph-based deep learning method for weed recognition. Comput. Electron. Agric. 174, 105520 (2020)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Huertas-Tato, J., Martín, A., Fierrez, J., Camacho, D.: Fusing CNNs and statistical indicators to improve image classification. Inf. Fusion 79, 174–187 (2022)
Mohammed, A., Kora, R.: A comprehensive review on ensemble deep learning: opportunities and challenges. J. King Saud Univ.-Comput. Inf. Sci. 35, 757–774 (2023)
Olsen, A., et al.: DeepWeeds: a multiclass weed species image dataset for deep learning. Sci. Rep. 9(1), 2058 (2019)
Rai, N., et al.: Applications of deep learning in precision weed management: a review. Comput. Electron. Agric. 206, 107698 (2023)
Saleem, M.H., Potgieter, J., Arif, K.M.: Weed detection by faster RCNN model: an enhanced anchor box approach. Agronomy 12(7), 1580 (2022)
Sharma, S., Vardhan, M.: Self-attention vision transformer with transfer learning for efficient crops and weeds classification. In: 2023 6th International Conference on Information Systems and Computer Networks (ISCON), pp. 1–6. IEEE (2023)
Wang, W., et al.: Pyramid vision transformer: a versatile backbone for dense prediction without convolutions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 568–578 (2021)
Wang, W., et al.: PVT v2: improved baselines with pyramid vision transformer. Comput. Vis. Media 8(3), 415–424 (2022)
Acknowledgement
This study was carried out within the Agritech National Research Center and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) - MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4-D.D. 1032 17/06/2022, CN00000022). This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them. This work was also partially funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, and project NextGenAI - Center for Responsible AI (2022-C05i0102-02), supported by IAPMEI, and also by FCT plurianual funding for 2020-2023 of LIACC (UIDB/00027/2020 UIDP/00027/2020). The authors gratefully acknowledge the financial support of National Council for Scientific and Technological Development - CNPq (Grants 311404/2021-9 and 313643/2021-0).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Springer Nature Switzerland AG
About this paper
Cite this paper
Rozendo, G.B., Roberto, G.F., do Nascimento, M.Z., Alves Neves, L., Lumini, A. (2024). Weeds Classification with Deep Learning: An Investigation Using CNN, Vision Transformers, Pyramid Vision Transformers, and Ensemble Strategy. In: Vasconcelos, V., Domingues, I., Paredes, S. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2023. Lecture Notes in Computer Science, vol 14469. Springer, Cham. https://doi.org/10.1007/978-3-031-49018-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-49018-7_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49017-0
Online ISBN: 978-3-031-49018-7
eBook Packages: Computer ScienceComputer Science (R0)
