Keywords: paralog processing, phenotyping, image-based phenotyping

Latest 6 Papers

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

Unravelling the intraspecific variation in drought responses in seedlings of European black pine (Pinus nigra J.F. Arnold)

Authors: Ahmad, M., Hammerbacher, A., Priemer, C., Ciceu, A., Karolak, M., Mader, S., Olsson, S., Schinnerl, J., Seitner, S., Schoendorfer, S., Helfenbein, P., Jakub, J., Breuer, M., Espinosa, A., Caballero, T., Ganthaler, A., Mayr, S., Grosskinsky, D. K., Wienkoop, S., Schueler, S., Trujillo-Moya, C., van Loo, M.

Date: 2025-10-21 · Version: 1

DOI: 10.1101/2025.10.20.683360

Model Organism: Pinus nigra

drought tolerance Pinus nigra metabolomics transcriptomics phenotyping

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

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

The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset

Authors: Wang, Z., Zenkl, R., Greche, L., De Solan, B., Bernigaud Samatan, L., Ouahid, S., Visioni, A., Robles-Zazueta, C. A., Pinto, F., Perez-Olivera, I., Reynolds, M. P., Zhu, C., Liu, S., D'argaignon, M.-P., Lopez-Lozano, R., Weiss, M., Marzougui, A., Roth, L., Dandrifosse, S., Carlier, A., Dumont, B., Mercatoris, B., Fernandez, J., Chapman, S., Najafian, K., Stavness, I., Wang, H., Guo, W., Virlet, N., Hawkesford, M., Chen, Z., David, E., Gillet, J., Irfan, K., Comar, A., Hund, A.

Date: 2025-03-19 · Version: 1

DOI: 10.1101/2025.03.18.642594

Model Organism: Triticum aestivum

wheat semantic segmentation computer vision deep learning phenotyping