ML Anthology

Locating Crop Plant Centers from UAV-Based RGB Imagery

Yuhao Chen, Javier Ribera, Christopher Boomsma, Edward J. Delp
ICCVW 2017 pp. 2030-2037
doi:10.1109/ICCVW.2017.238 /iccvw/2017/chen2017iccvw-locating/

Abstract

In this paper we propose a method to find the location of crop plants in Unmanned Aerial Vehicle (UAV) imagery. Finding the location of plants is a crucial step to derive and track phenotypic traits for each plant. We describe some initial work in estimating field crop plant locations. We approach the problem by classifying pixels as a plant center or a non plant center. We use Multiple Instance Learning (MIL) to handle the ambiguity of plant center labeling in training data. The classification results are then post-processed to estimate the exact location of the crop plant. Experimental evaluation is conducted to evaluate the method and the result achieved an overall precision and recall of 66% and 64%, respectively.

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Cite

Text

Chen et al. "Locating Crop Plant Centers from UAV-Based RGB Imagery." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.238

Markdown

[Chen et al. "Locating Crop Plant Centers from UAV-Based RGB Imagery." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/chen2017iccvw-locating/) doi:10.1109/ICCVW.2017.238

BibTeX

@inproceedings{chen2017iccvw-locating,
  title     = {{Locating Crop Plant Centers from UAV-Based RGB Imagery}},
  author    = {Chen, Yuhao and Ribera, Javier and Boomsma, Christopher and Delp, Edward J.},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2017},
  pages     = {2030-2037},
  doi       = {10.1109/ICCVW.2017.238},
  url       = {https://mlanthology.org/iccvw/2017/chen2017iccvw-locating/}
}