Keywords: paralog processing, ascorbate, transcriptome reprogramming, deep learning, Triticum aestivum

Latest 27 Papers

Explainable AI shows climate impacts on wheat yields: insights from 30 years of field data.

Authors: Visse-Mansiaux, M., Ryo, M., Burton, A., Siraj, T., Schiller, J., Treier, S., Pellet, D., Stefan, L., Levy Haener, L., Herrera, J. M., Wanger, T. C.

Date: 2025-09-21 · Version: 1

DOI: 10.1101/2025.09.19.677331

Model Organism: Triticum aestivum

Triticum aestivum climate-driven yield variability explainable artificial intelligence gradient boosting genotype‑by‑environment interaction

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

Choosing the Best Route: Comparative Optimization of Wheat Transformation Methods for Improving Yield by Targeting TaARE1-D with CRISPR/Cas9

Authors: Tek, M. I., Budak Tek, K., Sarikaya, P., Ahmed, A. R., Fidan, H.

Date: 2025-09-12 · Version: 1

DOI: 10.1101/2025.09.11.675438

Model Organism: Triticum aestivum

Triticum aestivum CRISPR/Cas9 Agrobacterium-mediated transformation TaARE1-D yield improvement

The Rapid Anatomics Tool (RAT): A low-cost root anatomical phenotyping pipeline reveals changes in root anatomy along the root axis

Authors: Jones, D. H., Baca Cabrera, J. C., Behrend, D., Wells, D. M., Swift, J. F., Atkinson, J., Lobet, G., Hanlon, M. T., Schneider, H. M.

Date: 2025-09-07 · Version: 1

DOI: 10.1101/2025.09.05.674482

Model Organism: Triticum aestivum

root anatomy near‑UV autofluorescence high‑throughput imaging blockface imaging Triticum aestivum

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

Mycotoxin-driven proteome remodeling reveals limited activation of Triticum aestivum responses to emerging chemotypes integrated with fungal modulation of ergosterols

Authors: Ramezanpour, S., Alijanimamaghani, N., McAlister, J. A., Hooker, D., Geddes-McAlister, J.

Date: 2025-07-01 · Version: 1

DOI: 10.1101/2025.06.30.662327

Model Organism: Triticum aestivum

Fusarium head blight 15ADON/3ANX chemotype proteomics Triticum aestivum Fusarium graminearum

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

Scaling law links plant growth variation to grain yield in wheat stands

Authors: Golan, G., Vasseur, F., Huang, Y., Tan, K., Sadras, V., violle, c., Schnurbusch, T.

Date: 2025-05-23 · Version: 1

DOI: 10.1101/2025.05.19.654792

Model Organism: Triticum aestivum

growth allometry Triticum aestivum Photoperiod response-1 (Ppd-1)genotype-by-environment interaction grain yield

Arabidopsis lines with modified ascorbate concentrations reveal a link between ascorbate and auxin biosynthesis

Authors: Fenech, M., Zulian, V., Moya-Cuevas, J., Arnaud, D., Morilla, I., Smirnoff, N., Botella, M. A., Stepanova, A. N., Alonso, J. M., Martin-Pizarro, C., Amorim-Silva, V.

Date: 2025-05-16 · Version: 1

DOI: 10.1101/2025.05.15.654287

Model Organism: Arabidopsis thaliana

ascorbate Arabidopsis thaliana auxin biosynthesis redox homeostasis transcriptomics

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