Keywords: decoding modules, SNP array, CO2-fixing enzyme, computer vision, hyperspectral imaging

Latest 11 Papers

Universal modules for decoding amplitude and frequency of Ca2+ signals in plants

Authors: Vergara-Valladares, F., Rubio-Melendez, M. E., Charpentier, M., Michard, E., Dreyer, I.

Date: 2025-12-16 · Version: 1

DOI: 10.64898/2025.12.13.694100

Model Organism: General

calcium signaling EF‑hand Ca2+ binding protein decoding modules plant calcium sensors signal amplitude and frequency

Automated detection and quantification of two-spotted spider mite life stages using computer vision for high-throughput in vitro assays

Authors: Wiseman, M. S., Woods, J. L., Hartgrave, C. R., Richardson, B. J., Gent, D. H.

Date: 2025-12-10 · Version: 2

DOI: 10.1101/2025.09.14.676116

Model Organism: Tetranychus urticae

Tetranychus urticae high‑throughput phenotyping computer vision YOLO11 object detection resistance breeding

Rubisco Dark Inhibition in Angiosperms Shows a Complex Distribution Pattern

Authors: Nehls-Ramos, C., Carmo-Silva, E., Orr, D. J.

Date: 2025-11-20 · Version: 1

DOI: 10.1101/2025.11.20.689527

Model Organism: Multi-species

Rubisco dark inhibition flowering plants phylogenetic analysis photosynthetic regulation CO2-fixing enzyme

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

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

A century of breeding has preserved genetic variation, accumulated favorable alleles, and shaped the Rht gene portfolio in North American spring wheat

Authors: Gill, H. S., Blecha, S., Conley, E., Brault, C., Fiedler, J., Cook, J., Glover, K., Green, A., Read, A. C., Anderson, J. A.

Date: 2025-09-23 · Version: 1

DOI: 10.1101/2025.09.22.677111

Model Organism: Triticum aestivum

Hard red spring wheat genetic diversity Rht dwarfing genes SNP array KASP genotyping

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

Remote sensing of endogenous pigmentation by inducible synthetic circuits in grasses

Authors: Lee, D.-Y., Acosta-Gamboa, L., Saleh, L., Pathak, S., Swyers, N., Morgan, A., Meerdink, S., Kuzio, C., Calderon, S., Sheng, H., Kenney, S., Zare, A., Gehan, M., Nusinow, D. A.

Date: 2025-06-23 · Version: 1

DOI: 10.1101/2025.06.20.660755

Model Organism: Setaria viridis

synthetic biology monocot plant engineering ligand‑inducible sensor anthocyanin pathway hyperspectral imaging

Evaporative cooling signals for wound healing in plants

Authors: Balem, J. M., Tan, C., Dias, N. C. F., Arnold, M. L., Tran, S., Severns, P. M., Teixeira, P. J. P. L., Li, C., Yang, L.

Date: 2025-05-28 · Version: 1

DOI: 10.1101/2025.05.23.655667

Model Organism: Arabidopsis thaliana

wound healing Arabidopsis thaliana cold-responsive genes CBF transcription factors computer vision

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

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