Keywords: paralog processing, CO2-fixing enzyme, deep learning, high‑throughput phenotyping

Latest 21 Papers

Linking stomatal function with photosynthetic light reactions and stress response in faba bean

Authors: Shapiguzov, A., Punkkinen, M., Laine, T., Engstrom, S., Aphalo, P. J., Khazaei, H.

Date: 2025-09-02 · Version: 1

DOI: 10.1101/2025.08.29.673109

Model Organism: Vicia faba

Vicia faba stomatal function leaf temperature proxy ozone stress high‑throughput phenotyping

A sublethal drought and rewatering time course reveals intricate patterning of responses in the annual Arabidopsis thaliana

Authors: Fitzek-Campbell, E., Psaroudakis, D., Weisshaar, B., Junker, A., Braeutigam, A.

Date: 2025-07-27 · Version: 1

DOI: 10.1101/2025.07.25.666782

Model Organism: Arabidopsis thaliana

drought stress Arabidopsis thaliana transcriptomics high‑throughput phenotyping biomarker transcripts

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

RegenPGC View: Semantic Segmentation of Perennial Groundcover Cropping Systems to Restore American Croplands

Authors: Meyering, B., Schlautman, B.

Date: 2025-07-06 · Version: 1

DOI: 10.1101/2025.07.03.663038

Model Organism: General

perennial groundcovers cover cropping deep learning convolutional neural network image‑based phenotyping

Unraveling the cis-regulatory code controlling abscisic acid-dependent gene expression in Arabidopsis using deep learning

Authors: Opdebeeck, H., Smet, D., Thierens, S., Minne, M., De Beukelaer, H., Zuallaert, J., Van Bel, M., Van Montagu, M., Degroeve, S., De Rybel, B., Vandepoele, K.

Date: 2025-06-06 · Version: 1

DOI: 10.1101/2025.06.03.657584

Model Organism: Arabidopsis thaliana

Abscisic acid Arabidopsis thaliana deep learning promoter motifs ABF binding sites

Prediction of harvest-related traits in barley using high-throughput phenotyping data and machine learning

Authors: Tietze, H., Abdelhakim, L., Pleskacova, B., Kurtz-Sohn, A., Fridman, E., Nikoloski, Z., Panzarova, K.

Date: 2025-06-02 · Version: 1

DOI: 10.1101/2025.05.29.656856

Model Organism: Hordeum vulgare

high‑throughput phenotyping drought stress temporal modeling Barley (Hordeum vulgare)RGB imaging

Spatial analysis of cell patterning to aid genetic and phenotypic understanding of grass stomatal density: a case study in maize

Authors: Hodge, J. G., Leakey, A. D. B.

Date: 2025-05-23 · Version: 1

DOI: 10.1101/2025.05.21.655366

Model Organism: Zea mays

stomatal density high‑throughput phenotyping spatial analysis quantitative trait loci structural equation modeling

Deciphering the Sequence Basis and Application of Transcriptional Initiation Regulation in Plant Genomes Through Deep Learning

Authors: Gao, P., Lian, L., Feng, W., Ma, Y., Lin, J., Qin, L., Hao, S., Zhao, H., Liu, X., Yuan, J., Lin, Z., Li, X., Guan, Y., Wang, X.

Date: 2025-04-24 · Version: 1

DOI: 10.1101/2025.04.22.649999

Model Organism: Glycine max

transcription start site promoter motifs deep learning soybean domestication plant gene regulation

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

The FIP 1.0 Data Set: Highly Resolved Annotated Image Time Series of 4,000 Wheat Plots Grown in Six Years

Authors: Roth, L., Boss, M., Kirchgessner, N., Aasen, H., Aguirre-Cuellar, B. P., Akiina, P. P. A., Anderegg, J., Gajardo Castillo, J., Chen, X., Corrado, S., Cybulski, K., Keller, B., Kortstee, S. G., Kronenberg, L., Liebisch, F., Nousi, P., Oppliger, C., Perich, G., Pfeifer, J., Yu, K., Storni, N., Tschurr, F., Volpi, M., Treier, S., Zellweger, H., Zumsteg, O., Hund, A., Walter, A.

Date: 2025-03-11 · Version: 3

DOI: 10.1101/2024.10.04.616624

Model Organism: Triticum aestivum

genotype‑environment interaction winter wheat high‑throughput phenotyping genomic prediction crop modeling

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