Keywords: paralog processing, CO2 enrichment, ascorbate, transcriptome, deep learning, image‑based phenotyping

Latest 17 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

Semi-automated image analysis of root architecture and early root development in faba bean and white clover and genomic estimation of breeding values and correlations

Authors: Nagy, I., Kristensen, P. S., Malinowska, M., Nielsen, L. K., Schiemann, A., Rolund, N., Andersen, S. U., Asp, T.

Date: 2025-12-03 · Version: 1

DOI: 10.64898/2025.12.01.691692

Model Organism: Multi-species

leguminous protein crops climate‑resilient breeding root architecture image‑based phenotyping genomic estimated breeding values

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

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

Leveraging Crop Wild Relatives and Ploidy Level for Climate-Resilient Annual Ryegrass

Authors: Mailhos, M. E., Timmers, J., Erickson, J., Caram, N., Messina, C. D., Rios, E. F.

Date: 2025-10-18 · Version: 1

DOI: 10.1101/2025.10.15.682691

Model Organism: Lolium multiflorum

polyploidy climate change Lolium multiflorum CO2 enrichment evapotranspiration

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

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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