Keywords: paralog processing, ascorbate, spatial transcriptomics, wheat, deep learning

Latest 35 Papers

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

Regenerative agriculture effects on biomass, drought resilience and 14C-photosynthate allocation in wheat drilled into ley compared to disc or ploughed arable soil

Authors: Austen, N., Short, E., Tille, S., Johnson, I., Summers, R., Cameron, D. D., Leake, J. R.

Date: 2025-09-07 · Version: 1

DOI: 10.1101/2025.09.04.674292

Model Organism: Triticum aestivum

regenerative agriculture soil macroaggregation wheat 14C pulse labeling drought resilience

Spatial transcriptomics identifies distinct domains regulating yield-related traits of the wheat ear

Authors: Qu, Y., Tan, C., Yang, L., Pasquariello, M., Alabdullah, A. K., Sun, S., Iqbal, M., Salamon, J., Boden, S.

Date: 2025-08-13 · Version: 1

DOI: 10.1101/2025.08.12.670006

Model Organism: Triticum aestivum

spikelet development spatial transcriptomics wheat inflorescence RAMOSA2 ALOG1

Soil phosphate availability modulates the arbuscular mycorrhizal fungal community and mycorrhizal nutrition in wheat

Authors: Trinquier, M., Lecloux, E., Bruno, P., Gasciolli, V., Jouany, C., Roux, C., Lefebvre, B., Ardanuy, A.

Date: 2025-08-09 · Version: 1

DOI: 10.1101/2025.08.08.669323

Model Organism: Triticum aestivum

arbuscular mycorrhizal fungi phosphorus availability nitrogen availability wheat metabarcoding

An aphid resistant wheat variety reduces the transmission of Barley Yellow Dwarf Virus (BYDV) by Rhopalosiphum padi (L.)

Authors: Qonaah, I. A., Simon, A. L., Warner, D., Bruce, T. J. A., Ray, R. V.

Date: 2025-08-02 · Version: 1

DOI: 10.1101/2025.07.30.667415

Model Organism: Triticum aestivum

aphid resistance Barley Yellow Dwarf Virus wheat 2‑tridecanone virus transmission

Plant-Compatible Xenium In Situ Sequencing: Optimised Protocol for Spatial Transcriptomics in Medicago truncatula Roots and Nodules

Authors: Jhu, M.-Y., Heffer, J., Deamer, A., Moraes, T. A., Piskorz, A. M., Xia, C.

Date: 2025-07-25 · Version: 1

DOI: 10.1101/2025.07.22.663073

Model Organism: Medicago truncatula

spatial transcriptomics Xenium in situ sequencing Medicago truncatula root nodules autofluorescence mitigation

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

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