Keywords: paralog processing, deep learning, ascorbate, spatial transcriptomics, image-based phenotyping

Latest 22 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

Regulation of spikelet number during wheat spike development

Authors: Li, C., Li, K., Zhang, C., Dubcovsky, J.

Date: 2026-01-08 · Version: 1

DOI: 10.64898/2026.01.07.698073

Model Organism: Triticum aestivum

spikelet number per spike wheat inflorescence development florigen (FT1)spatial transcriptomics single‑cell analysis

PlantCV v4: Image analysis software for high-throughput plant phenotyping

Authors: Schuhl, H., Brown, K. E., Sheng, H., Bhatt, P. K., Gutierrez, J., Schneider, D., Casto, A. L., Acosta-Gamboa, L., Ballenger, J. G., Barbero, F., Braley, J., Brown, A. M., Chavez, L., Cunningham, S., Dilhara, M., Dimech, A. M., Duenwald, J. G., Fischer, A., Gordon, J. M., Hendrikse, C., Hernandez, G. L., Hodge, J. G., Huber, M., Hurr, B. M., Jarolmasjed, S., Medina Jimenez, K., Kenney, S., Konkel, G., Kutschera, A., Lama, S., Lohbihler, M., Lorence, A., Luebbert, C., Ly, N., Manching, H. K., Marrano, A., Meerdink, S., Miklave, N. M., Mudrageda, P., Murphy, K. M., Peery, J. D., Pierik, R., Polyd

Date: 2025-11-20 · Version: 1

DOI: 10.1101/2025.11.19.689271

Model Organism: General

PlantCV image-based phenotyping fluorescence imaging thermal imaging hyperspectral imaging

Integrating image-based phenotyping and GWAS to map tolerance to Spittlebug nymphs in interspecific Urochloa grasses

Authors: Espitia-Buitrago, P., Perea, C., Mejia-Medina, J. C., Hernandez, L. M., Castiblanco, V., Ryan, C., De Vega, J. J., Jauregui, R. N.

Date: 2025-11-05 · Version: 1

DOI: 10.1101/2025.11.03.686394

Model Organism: Urochloa spp.

Urochloa spittlebug tolerance GWAS QTL image-based phenotyping

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

Spatiotemporal regulation of arbuscular mycorrhizal symbiosis at cellular resolution

Authors: Chancellor, T., Ferreras-Garrucho, G., Akmakjian, G. Z., Montero, H., Bowden, S. L., Hope, M., Wallington, E., Bhattacharya, S., Korfhage, C., Bailey-Serres, J., Paszkowski, U.

Date: 2025-11-02 · Version: 1

DOI: 10.1101/2025.10.31.685811

Model Organism: Oryza sativa

arbuscular mycorrhizal symbiosis spatial transcriptomics TRAP-seq arbuscule development nutrient transport

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

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