AUTHOR=Alle Jonas , Gruber Roland , Wörlein Norbert , Uhlmann Norman , Claußen Joelle , Wittenberg Thomas , Gerth Stefan 

TITLE=3D segmentation of plant root systems using spatial pyramid pooling and locally adaptive field-of-view inference

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1120189

DOI=10.3389/fpls.2023.1120189

ISSN=1664-462X

ABSTRACT=Background: The non-invasive 3D-imaging and successive 3D-segmentation of plant root systems has gained interest within fundamental plant research and selectively breeding resilient crops. Currently the state of the art consists of computed tomography (CT) scans and -reconstruction followed by an adequate 3D-segmentation process. 
Challenge: Generating an exact 3D-segmentation of the roots becomes challenging due to inhomogeneous soil composition, as well as high scale-variance in the root structures themselves.
Approach: (1) We address the challenge by combining deep convolutional neural networks (DCNNs) with a weakly supervised learning paradigm. Furthermore, (2) we apply a spatial pyramid pooling (SPP) layer to cope with the scale variance of roots. (3) We generate a fine-tuned training data set with a specialized sub-labeling technique. (4) Finally, to yield fast and high-quality segmentations, we propose a specialized iterative inference algorithm, which locally adapts the field of view (FoV) for the network.
Experiments: We compare our segmentation results against an analytical reference algorithm for root segmentation (RootForce) on a set of roots from Cassava plants and show qualitatively that an increased amount of root voxels and root branches can be segmented.
Results: Our findings show that with the proposed DCNN approach combined with the dynamic inference much more, and especially fine root structures can be detected than with a classical analytical reference method.
Conclusion: We show that the application of the proposed DCNN approach leads to better and more robust root segmentation, especially for very small and thin roots.