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Multi-modal and multi-view image dataset for weeds detection in wheat field



doi: 10.3389/fpls.2022.936748.


eCollection 2022.

Affiliations

Item in Clipboard

Ke Xu et al.


Front Plant Sci.


.

No abstract available


Keywords:

deep learning; grass weeds detection; machine learning; multi-modal image; multi-view image; wheat field.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures



Figure 1

(A) Image acquisition equipment, (B) Intel® RealSense™ Depth Camera D415, and (C) TL-IPC44AN-4camera.


Figure 2


Figure 2

(A) Labeling of grass and broadleaf weeds in wheat fields using LabelImg and (B) weed detection result in wheat field.

References

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    1. Chebrolu N., Lottes P., Schaefer Winterhalter, Burgard Stachniss. (2017). Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields. Int. J. Robot Res. 36, 1045–1052. 10.1177/0278364917720510



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    1. Coleman G. (2021). 20201014 – Cobbity Wheat BFLY. Weed-AI. Available online at: https://weed-ai.sydney.edu.au/datasets/73468c19-b098-406a-86fa-df172caaec16.

    1. Espejo-Garcia B., Mylonas N., Athanasakos L., Fountas S., Vasilakoglou I. (2020). Towards weeds identification assistance through transfer learning. Comput. Electron. Agric. 171, 105306. 10.1016/j.compag.2020.105306



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    1. Fahad S., Hussain S., Chauhan B. S., Saud S., Wu C., Hassan S., et al. . (2015). Weed growth and crop yield loss in wheat as influenced by row spacing and weed emergence times. Crop Protect. 71, 101–108. 10.1016/j.cropro.2015.02.005



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