Keywords: paralog processing, deep learning, CLPC2, ascorbate, bread wheat

Latest 16 Papers

Deep learning of fossil pollen morphology reveals 25,000 years of ecological change in East African grasslands

Authors: Adaime, M.-E., Kong, S., Urban, M. A., Street-Perrott, F. A., Verschuren, D., Punyasena, S. W.

Date: 2026-01-22 · Version: 3

DOI: 10.1101/2024.09.23.612957

Model Organism: General

deep learning grass pollen morphology C3/C4 plant ratios paleo‑vegetation reconstruction super‑resolution microscopy

Reconstructing coniferous tree crown shape from incomplete point clouds using deep learning

Authors: Bornand, A., Abegg, M., Morsdorf, F., Puliti, S., Astrup, R., Rehush, N.

Date: 2026-01-21 · Version: 1

DOI: 10.64898/2026.01.18.700158

Model Organism: General

deep learning point cloud reconstruction alpha‑shape coniferous tree crowns geometry-aware transformer

Root phenolics as potential drivers of preformed defenses and reduced disease susceptibility in a paradigm bread wheat mixture

Authors: Mathieu, L., Chloup, A., Marty, S., Savajols, J., Paysant-Le Roux, C., Launay-Avon, A., Martin, M.-L., Totozafy, J.-C., Perreau, F., Rochepeau, A., Rouveyrol, C., Petriacq, P., Morel, J.-B., Meteignier, L.-V., Ballini, E.

Date: 2026-01-14 · Version: 1

DOI: 10.64898/2026.01.13.699261

Model Organism: Triticum aestivum

root-mediated interactions bread wheat Septoria tritici blotch transcriptomics metabolomics

CLPC2 plays specific roles in CLP complex-mediated regulation of growth, photosynthesis, embryogenesis and response to growth-promoting microbial compounds

Authors: Leal-Lopez, J., Bahaji, A., De Diego, N., Tarkowski, P., Baroja-Fernandez, E., Munoz, F. J., Almagro, G., Perez, C. E., Bastidas-Parrado, L. A., Loperfido, D., Caporalli, E., Ezquer, I., Lopez-Serrano, L., Ferez-Gomez, A., Coca-Ruiz, V., Pulido, P., Morcillo, R. J. L., Pozueta-Romero, J.

Date: 2025-11-28 · Version: 1

DOI: 10.1101/2025.11.25.690394

Model Organism: Arabidopsis thaliana

CLPC2 microbial volatile compounds chloroplast CLP protease proteomics Arabidopsis thaliana

Cross-dataset evaluation of deep learning models for plant pest and disease diagnosis

Authors: Sun, J.

Date: 2025-11-03 · Version: 2

DOI: 10.1101/2024.10.07.617111

Model Organism: Solanum lycopersicum

deep learning cross-dataset generalization tomato pest and disease dataset variability model architecture

Hyperspectral Imaging to Quantify Nodules and Detect Biological Nitrogen Fixation in Legumes

Authors: Wang, Y., de Silva, K., Song, D., Kamruzzaman, M., Brooks, M. D.

Date: 2025-10-24 · Version: 3

DOI: 10.1101/2025.07.25.666867

Model Organism: Multi-species

root nodules biological nitrogen fixation hyperspectral imaging deep learning legume species

Phylogenomic challenges in polyploid-rich lineages: Insights from paralog processing and reticulation methods using the complex genus Packera (Asteraceae: Senecioneae)

Authors: Moore-Pollard, E. R., Ellestad, P., Mandel, J.

Date: 2025-10-13 · Version: 2

DOI: 10.1101/2025.09.14.676143

Model Organism: Multi-species

phylogenomic discordance polyploidy paralog processing Packera historical biogeography

Botanic Spectrum Analyser: A Deep Learning GUI for Plant Image Segmentation in Hyperspectral and RGB Phenotyping

Authors: Walsh, J. J., Gorgu, L., Cavel, E., Poulain, V., Gutierrez, L., Mangina, E., Negrao, S.

Date: 2025-09-17 · Version: 1

DOI: 10.1101/2025.09.14.676080

Model Organism: Multi-species

plant phenotyping image segmentation U‑Net hyperspectral imaging deep learning

Controlling GRF4-GIF1 Expression for Efficient, Genotype-Independent Transformation Across Wheat Cultivars

Authors: Hayta, S., Smedley, M. A., Bayraktar, M., Forner, M., Backhaus, A., Lister, C., Clarke, M., Uauy, C., Griffiths, S.

Date: 2025-09-02 · Version: 1

DOI: 10.1101/2025.08.29.673042

Model Organism: Triticum aestivum

GRF4‑GIF1 fusion Agrobacterium-mediated transformation genotype‑independent regeneration bread wheat transformation efficiency

Phenotypic scoring of Canola Blackleg severity using machine learning image analysis

Authors: Hu, Q., Anderson, S. N., Gardner, S., Ernst, T. W., Koscielny, C. B., Bahia, N. S., Johnson, C. G., Jarvis, A. C., Hynek, J., Coles, N., Falak, I., Charne, D. R., Ruidiaz, M. E., Linares, J. N., Mazis, A., Stanton, D. J.

Date: 2025-07-22 · Version: 1

DOI: 10.1101/2025.07.17.665372

Model Organism: Multi-species

blackleg disease canola (Brassica spp.)deep learning image-based phenotyping disease severity scoring

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