Automated morphological trait analysis of plants in early ontogeny for breeding applications using deep learning segmentation and graph-based analysis

Avraham E.M., Kreimer A.S.
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We propose an approach for the reconstruction and quantitative phenotyping of plant morphological traits at early ontogenetic stages based on digital image analysis. The proposed algorithm combines deep learning and graph-based representations while incorporating biological principles of morphogenesis. This integration enables the transition from binary segmentation to the reconstruction of a topologically consistent plant structure with accurate separation of intersecting root systems in images containing multiple plants. At the first stage, binary masks of seeds, shoots, and root systems are generated using a U-Net convolutional neural network architecture. The resulting image is transformed into a graph model in which edges correspond to root and shoot segments, while vertices represent key morphological points, including branching and intersection nodes. A directed traversal algorithm initialized from the seed point, combined with watershed-based graph partitioning and a composite scoring function for primary axis selection, identifies individual plants as isolated subgraphs and accurately distinguishes the primary root, lateral roots, and shoot. The validity of the algorithm was confirmed through a multi-level validation procedure, including comparison of graph reconstruction results on segmented images and end-to-end evaluation of the complete computational pipeline on original images against existing software solutions and expert manual annotations. The proposed approach is robust to variability in root system morphology and image noise and provides high accuracy in morphological trait extraction. The results of this study can be applied in plant breeding programs.

Keywords: neural networks, deep learning, U-Net, graph-based modeling, plant phenotyping, root architecture, morphological analysis, breeding
Citation in English: Avraham E.M., Kreimer A.S. Automated morphological trait analysis of plants in early ontogeny for breeding applications using deep learning segmentation and graph-based analysis // Computer Research and Modeling, 2026, vol. 18, no. 4, pp. 891-908
Citation in English: Avraham E.M., Kreimer A.S. Automated morphological trait analysis of plants in early ontogeny for breeding applications using deep learning segmentation and graph-based analysis // Computer Research and Modeling, 2026, vol. 18, no. 4, pp. 891-908
DOI: 10.20537/2076-7633-2026-18-4-891-908
URL: http://crm-en.ics.org.ru/journal/article/3751/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Copyright © 2026 Avraham E.M., Kreimer A.S.

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