CNN based Heuristic Function for A* Pathfinding Algorithm: Using Spatial Vector Data to Reconstruct Smooth and Natural Looking Plant Roots

Abstract

In this work we propose an extension to recent methods for the reconstruction of root architectures in 2-dimensions. Recent methods for the automatic root analysis have proposed deep learned segmentation of root images followed by path finding such as Dijkstras algorithm to reconstruct root topology. These approaches assume that roots are separate, and that a shortest path within the image foreground represents a reliable reconstruction of the underlying root structure. This approach is prone to error where roots grow in close proximity, with path finding algorithms prone to taking "short cuts" and overlapping much of the root material. Here we extend these methods to also consider root angle, allowing a more informed shortest path search that disambiguates roots growing close together. We adapt a CNN architecture to also predict the angle of root material at each foreground position, and utilise this additional information within shortest path searchers to improve root reconstruction. Our results show an improved ability to separate clustered roots.

Code and data availability

The paper uses the RootNav 2.0 dataset and pretrained model from prior work [1], but contains no explicit public availability statement, deposit, or authors' URL for its own data, code, or trained models. No qualifying paper-specific public assets are present, and allowed_urls is empty.

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