Tracking Growth and Decay of Plant Roots in Minirhizotron Images
Abstract
Plant roots are difficult to monitor and study since they are hidden belowground. Minirhizotrons offer an in-situ monitoring solution but their widespread adoption is still limited by the capabilities of automatic analysis methods. These capabilities so far consist only of estimating a single number (total root length) per image.\nWe propose a method for a more fine-grained analysis which estimates the root turnover, i.e. the amount of root growth and decay between two minirhizotron images. It consists of a neural network that computes which roots are visible in both images and is trained in an unsupervised manner without additional annotations.\nOur code is available as a part of an analysis tool with a user interface ready to be used by ecologists.
Cite
Text
Gillert et al. "Tracking Growth and Decay of Plant Roots in Minirhizotron Images." Winter Conference on Applications of Computer Vision, 2023.Markdown
[Gillert et al. "Tracking Growth and Decay of Plant Roots in Minirhizotron Images." Winter Conference on Applications of Computer Vision, 2023.](https://mlanthology.org/wacv/2023/gillert2023wacv-tracking/)BibTeX
@inproceedings{gillert2023wacv-tracking,
title = {{Tracking Growth and Decay of Plant Roots in Minirhizotron Images}},
author = {Gillert, Alexander and Peters, Bo and von Lukas, Uwe Freiherr and Kreyling, Jürgen and Blume-Werry, Gesche},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2023},
pages = {3699-3708},
url = {https://mlanthology.org/wacv/2023/gillert2023wacv-tracking/}
}