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Counting plant organs such as heads or tassels from outdoor imagery is a popular benchmark computer vision task in plant phenotyping, which has been previously investigated in the literature using state-of-the-art supervised deep learning techniques.
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: European conference on computer vision. pp. 740–755. Springer (2014)
2014
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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)
2016
Earlier work this paper cites.
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)
2017
Earlier work this paper cites.
Lu, H., Cao, Z., Xiao, Y., Zhuang, B., Shen, C.: TasselNet : Counting maize tassels in the wild via local counts regression network. Plant Methods pp. 1–14 (2017). https://doi.org/10.1186/s13007-017-0224-0
2017
Earlier work this paper cites.
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)
2017
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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
2017
Earlier work this paper cites.
Ubbens, J.R., Stavness, I.: Deep plant phenomics: a deep learning platform for complex plant phenotyping tasks. Frontiers in Plant Science 8
2017
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Xiong, X., Duan, L., Liu, L., Tu, H., Yang, P., Wu, D., Chen, G., Xiong, L., Yang, W., Liu, Q.: Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization. Plant Methods 13
2017
Cited alongside, same era.
Giuffrida, M.V., Chen, F., Scharr, H., Tsaftaris, S.A.: Citizen crowds and experts: observer variability in image-based plant phenotyping. Plant Methods 14
2018
Cited alongside, same era.
Guo, W., Zheng, B., Potgieter, A.B., Diot, J., Watanabe, K., Noshita, K., Jordan, D.R., Wang, X., Watson, J., Ninomiya, S., et al.: Aerial imagery analysis–quantifying appearance and number of sorghum heads for applications in breeding and agronomy. Frontiers in Plant Science 9
2018
Cited alongside, same era.
Hasan, M.M., Chopin, J.P., Laga, H., Miklavcic, S.J.: Detection and analysis of wheat spikes using Convolutional Neural Networks. Plant Methods 14
2018
Cited alongside, same era.
Rapin, J., Teytaud, O.: Nevergrad - A gradient-free optimization platform. https://GitHub.com/FacebookResearch/Nevergrad (2018)
2018
Later among the works it cites.
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
2018
Later among the works it cites.
Ghosal, S., Zheng, B., Chapman, S.C., Potgieter, A.B., Jordan, D.R., Wang, X., Singh, A.K., Singh, A., Hirafuji, M., Ninomiya, S., Ganapathysubramanian, B., Sarkar, S., Guo, W.: A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting. Plant Phenomics 2019
2019
Later among the works it cites.
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. Frontiers in Plant Science 10
2019
Later among the works it cites.
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Kanezaki, A.: Unsupervised image segmentation by backpropagation. In: 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). pp. 1543–1547. IEEE (2018)
2018
Cited alongside, same era.
Madec, S., Jin, X., Lu, H., De Solan, B., Liu, S., Duyme, F., Heritier, E., Baret, F.: Ear density estimation from high resolution RGB imagery using deep learning technique. Agricultural and Forest Meteorology 264
2018
Cited alongside, same era.
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)
2019
Later among the works it cites.
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
2019
Later among the works it cites.