Результаты поиска по 'root architecture':
Найдено статей: 2
  1. Kapitan D.Y., Ovchinnikov P.A., Soldatov K.S., Andriushchenko P.D., Kapitan V.U.
    Optimized machine learning methods for studying the thermodynamic behavior of complex spin systems
    Computer Research and Modeling, 2026, v. 18, no. 1, pp. 25-40

    This paper presents a systematic study of the application of convolutional neural networks (CNNs) as an efficient tool for the analysis of critical and low-temperature phase states in two dimensional spin system models. The problem of calculating the dependence of the average energy $\langle E\rangle_T^{}$ on the spatial distribution of exchange integrals $J_k^{}$ for the Edwards – Anderson model on a square lattice with frustrated interactions is considered.

    We further construct a single convolutional classifier of phase states of the ferromagnetic Ising model on square, triangular, honeycomb, and kagome lattices, trained on configurations generated by the Swendsen – Wang cluster algorithm. Сomputed temperature profiles of the averaged posterior probability of the high-temperature phase, form clear S-shaped curves that intersect in the vicinity of the theoretical critical temperatures and allow one to determine $T_c^{}$ for the kagome lattice without additional retraining.

    It is shown that convolutional models substantially reduce the root-mean-square error (RMSE) compared with fully connected architectures and efficiently capture complex correlations between thermodynamic characteristics and the structure of magnetic correlated systems.

    Keywords: Ising model, spin glass, machine learning, convolutional neural networks.
  2. 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, v. 18, no. 4, pp. 891-908

    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.

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