ProTractor: A Lightweight Ground Imaging and Analysis System for Early-Season Field Phenotyping

Abstract

Acquiring high-resolution images in the field for image-based crop phenotyping is typically performed by complicated, custom built "pheno-mobiles." In this paper, we demonstrate that large datasets of crop row images can be easily acquired with consumer cameras attached to a regular tractor. Localization and labeling of individual rows of plants are performed by a computer vision approach, rather than sophisticated real-time geo-location hardware on the tractor. We evaluate our approach for cropping rows of early-season plants from a Brassica carinata field trial where we achieve 100% recall and 99% precision. We also demonstrate a proof-of-concept plant counting method for our ProTractor system using an object detection network that achieves a mean average precision of 0.82 when detecting plants, and an R2 of 0.89 when counting plants. The ProTractor design and software are open source to advance the collection of large outdoor plant phenotyping datasets with inexpensive and easy to use acquisition systems.

Cite

Text

Higgs et al. "ProTractor: A Lightweight Ground Imaging and Analysis System for Early-Season Field Phenotyping." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00319

Markdown

[Higgs et al. "ProTractor: A Lightweight Ground Imaging and Analysis System for Early-Season Field Phenotyping." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/higgs2019cvprw-protractor/) doi:10.1109/CVPRW.2019.00319

BibTeX

@inproceedings{higgs2019cvprw-protractor,
  title     = {{ProTractor: A Lightweight Ground Imaging and Analysis System for Early-Season Field Phenotyping}},
  author    = {Higgs, Nico and Leyeza, Blanche and Ubbens, Jordan R. and Kocur, Josh and van der Kamp, William and Cory, Theron and Eynck, Christina and Vail, Sally and Eramian, Mark G. and Stavness, Ian},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2019},
  pages     = {2629-2638},
  doi       = {10.1109/CVPRW.2019.00319},
  url       = {https://mlanthology.org/cvprw/2019/higgs2019cvprw-protractor/}
}