# Segmentation of Roots in Soil with U-Net

## TL;DR

This paper presents an automated root segmentation method using U-Net CNN architecture, significantly improving accuracy and efficiency in root phenotyping from soil images compared to manual methods.

## Contribution

The study introduces a novel application of U-Net CNN for root segmentation in soil images, with a new dataset and superior performance over traditional filter-based methods.

## Key findings

- Achieved a Spearman correlation of 0.9748 with line-intersect counts.
- Obtained an $r^2$ of 0.9217 indicating high accuracy.
- Secured an $F_1$ score of 0.7, outperforming manual annotations.

## Abstract

Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in a diverse array of conditions. Phenotyping roots in soil is often challenging due to the roots being difficult to access and the use of time consuming manual methods. Rhizotrons allow visual inspection of root growth through transparent surfaces. Agronomists currently manually label photographs of roots obtained from rhizotrons using a line-intersect method to obtain root length density and rooting depth measurements which are essential for their experiments. We investigate the effectiveness of an automated image segmentation method based on the U-Net Convolutional Neural Network (CNN) architecture to enable such measurements. We design a data-set of 50 annotated Chicory (Cichorium intybus L.) root images which we use to train, validate and test the system and compare against a baseline built using the Frangi vesselness filter. We obtain metrics using manual annotations and line-intersect counts. Our results on the held out data show our proposed automated segmentation system to be a viable solution for detecting and quantifying roots. We evaluate our system using 867 images for which we have obtained line-intersect counts, attaining a Spearman rank correlation of 0.9748 and an $r^2$ of 0.9217. We also achieve an $F_1$ of 0.7 when comparing the automated segmentation to the manual annotations, with our automated segmentation system producing segmentations with higher quality than the manual annotations for large portions of the image.