Rootex 2.0: Multi-head deep learning and graph-based analysis for automated barley root phenotyping

Understanding plant root architecture under diverse environmental conditions is crucial for improving crop resilience and ensuring global food security. We present a fully automated method for segmenting barley root systems from high-resolution images and detecting keypoints such as tips and sources with high precision. At the core of our approach is DeepRoot-3H, a novel multi-head deep network built upon the DeepLabv3+ backbone, designed to jointly handle root segmentation and keypoint detection within a unified architecture. This integrated design enhances both the consistency and robustness of the outputs.

A dedicated post-processing stage further refines keypoint localization, effectively handling challenges such as dense root clusters and variability in image quality. The resulting predictions are then structured into a graph representation, on which a path-walking algorithm identifies biologically meaningful connections between tips and sources. This enables the generation of RSML files and the extraction of critical morphological traits.

To evaluate the system, we employ IoU and Dice scores for segmentation quality, alongside Euclidean and weighted distance metrics for tip and source detection. We also assess the biological consistency of the extracted traits—such as total root length, tortuosity, covered area, and outer angles—through correlation and discrepancy measures. Experimental results on a challenging benchmark dataset demonstrate significant improvements over existing techniques, confirming the effectiveness and reliability of our method for high-fidelity root system analysis.