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Cited by 15Smart Agriculture and AIAdvanced Neural Network ApplicationsLeaf Properties and Growth Measurement

The DOI matches its Crossref record.

AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images

Open access
Ubbens, Jordan R., Ayalew, Tewodros W., Shirtliffe, Steve, Josuttes, Anique, Pozniak, Curtis, Stavness, Ian·2020·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-65414-6_27
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Crossref · 10.1007/978-3-030-65414-6_27

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  • Models for automated plant phenotyping are typically trained on data collected in a limited number of regions, even for crops grown worldwide such as wheat.

    “These models are typically trained on data collected in a limited number of regions, even for crops grown worldwide such as wheat (Madec et al., 2019; Xiong et al., 2019; Ubbens et al., 2020; Ayalew et al., 2020).”

    From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Ubbens et al., 2020

Citations

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Research works printed but not linked: 18

  1. Dobrescu, A., Giuffrida, M.V., Tsaftaris, S.A.: Leveraging multiple datasets for deep leaf counting. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 2072–2079 (2017) · DOI
  2. Ghosal, S., et al.: A weakly supervised deep learning framework for sorghum head detection and counting. Plant Phenomics 2019, 1–14 (2019). https://doi.org/10.34133/2019/1525874 · DOI
  3. Giuffrida, M.V., Chen, F., Scharr, H., Tsaftaris, S.A.: Citizen crowds and experts: observer variability in image-based plant phenotyping. Plant Methods 14(1), 12 (2018) · DOI
  4. Giuffrida, M.V., Minervini, M., Tsaftaris, S.A.: Learning to count leaves in rosette plants. In: Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP) 2015. BMVA Press (2016) · DOI
  5. Guo, W., et al.: Aerial imagery analysis-quantifying appearance and number of sorghum heads for applications in breeding and agronomy. Front. Plant Sci. 9, 1544 (2018) · DOI
  6. Hasan, M.M., Chopin, J.P., Laga, H., Miklavcic, S.J.: Detection and analysis of wheat spikes using Convolutional Neural Networks. Plant Methods 14(1), 1–13 (2018). https://doi.org/10.1186/s13007-018-0366-8 · DOI
  7. Kanezaki, A.: Unsupervised image segmentation by backpropagation. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1543–1547. IEEE (2018) · DOI
  8. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48 · DOI · Paper page
  9. Lu, H., Cao, Z., Xiao, Y., Zhuang, B., Shen, C.: TasselNet: counting maize tassels in the wild via local counts regression network. Plant Methods 1–14 (2017). https://doi.org/10.1186/s13007-017-0224-0 · DOI
  10. Madec, S., et al.: Ear density estimation from high resolution RGB imagery using deep learning technique. Agric. Forest Meteorol. 264, 225–234 (2019). https://doi.org/10.1016/j.agrformet.2018.10.013 · DOI · Paper page
  11. Pound, M.P., Atkinson, J.A., Wells, D.M., Pridmore, T.P., French, A.P.: Deep learning for multi-task plant phenotyping. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 2055–2063 (2017) · DOI
  12. Qiongyan, L., Cai, J., Berger, B., Okamoto, M., Miklavcic, S.J.: Detecting spikes of wheat plants using neural networks with laws texture energy. Plant Methods 13(1), 83 (2017) · DOI
  13. Sadeghi-Tehran, P., Virlet, N., Ampe, E.M., Reyns, P., Hawkesford, M.J.: DeepCount: in-field automatic quantification of wheat spikes using simple linear iterative clustering and deep convolutional neural networks. Front. Plant Sci. 10, 1–16 (2019). https://doi.org/10.3389/fpls.2019.01176 · DOI
  14. Ubbens, J., Cieslak, M., Prusinkiewicz, P., Stavness, I.: The use of plant models in deep learning: an application to leaf counting in rosette plants. Plant Methods 14(1), 6 (2018). https://doi.org/10.1186/s13007-018-0273-z. https://plantmethods.biomedcentral.com/articles/10.1186/s13007-018-0273-z · DOI
  15. Ubbens, J.R., Stavness, I.: Deep plant phenomics: a deep learning platform for complex plant phenotyping tasks. Front. Plant Sci. 8, 1190 (2017) · DOI
  16. Valerio Giuffrida, M., Dobrescu, A., Doerner, P., Tsaftaris, S.A.: Leaf counting without annotations using adversarial unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019) · DOI
  17. Xiong, H., Cao, Z., Lu, H., Madec, S., Liu, L., Shen, C.: TasselNetv2: in - field counting of wheat spikes with context - augmented local regression networks. Plant Methods (2019). https://doi.org/10.1186/s13007-019-0537-2 · DOI · Paper page
  18. Xiong, X., et al.: Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization. Plant Methods 13(1), 104 (2017) · DOI

Cited by

Sorted by citations

  • WILDS: A Benchmark of in-the-Wild Distribution Shifts

    Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518

    “These models are typically trained on data collected in a limited number of regions, even for crops grown worldwide such as wheat (Madec et al., 2019; Xiong et al., 2019; Ubbens et al., 2020; Ayalew et al., 2020).”
    Self-citation

Metadata

Authors
Ubbens, J. R., Ayalew, T. W., Shirtliffe, S., Josuttes, A., Pozniak, C., & Stavness, I.
Affiliations
University of Saskatchewan (CA)
Corresponding author
Ubbens, Jordan R.
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2020
Pages
391-399
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-65414-6_27
OpenAlex
W3043292641
Cited by
15
References
0
Citations by year
2026: 1 · 2025: 3 · 2024: 2 · 2023: 3 · 2022: 2 · 2021: 3 · 2020: 1
FWCI
3.31
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
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
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Leaf Properties and Growth Measurement
Keywords
unsupervised segmentation, plant phenotyping, field photographs, sorghum, wheat

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