High-throughput plant phenotyping 

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High-throughput plant phenotyping 

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๐Ÿค– Can machine learning reveal the genetic and phenotypic signals that shaped lifeโ€™s history?

๐Ÿ”— A quartet-based approach for inferring phylogenetically informative features from genomic and phenomic data. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2025.08.015

๐Ÿ“š CSBJ: https://www.csbj.org/

#AI #DeepLearning #Bioinformatics #Phylogenetics #Genomics #Phenomics #EvolutionaryBiology #MachineLearning #ComputationalBiology #NeuralNetworks #ScienceInnovation #AIinBiology

@deNBI is the next days at the #EPPS25 in Bonn. We teamed up with #wggc for a workshop and with #phenet for a booth. #plant #phenomics

๐Ÿ†•Interview: In the accompanying blog post, the first author, Di Li, tell us about her and talks about the internal and external morphology of ๐‘‚๐‘œ๐‘๐‘’๐‘Ÿ๐‘Ž๐‘’๐‘Ž ๐‘๐‘–๐‘Ÿ๐‘œ๐‘–.
#phenomics #anatomy #MicroCT #3D #reconstruction

https://blog.myrmecologicalnews.org/2025/06/12/what-can-larval-morphology-teach-us-about-ant-evolution-and-development/

๐Ÿ“˜ Understanding node-specific root responses could help develop maize varieties with better water uptake in tough environments. (8/8)
๐Ÿ‘‰ https://doi.org/pp4c

#PlantScience #Maize #DroughtResistance #RootBiology #CropResilience #Phenomics #AoBpapers

๐ŸŒฟCheck the newly published article โ€˜Node of origin matters: comparative analysis of soil water limitation effects on nodal root anatomy in maize (Zea mays L.)โ€™ in @AnnBot by Tina Koehler and co-authors ๐Ÿงต(1/8)

๐Ÿ‘‰ https://doi.org/pp4c

#PlantScience #Maize #DroughtResistance #RootBiology #CropResilience #Phenomics

๐ŸƒNovel analysis techniques in dissecting the plant phenome
by Aaron J DeSalvio, Alper Adak, Mustafa A Arik, Nicholas R Shepard, Serina M DeSalvio, Seth C Murray, Oriana Garcรญa-Ramos, Himabindhu Badavath, David M Stelly
https://doi.org/pjhs #cotton #DeepLearning #phenomics #PlantScience

Planning #FAIR packaging of #plant #phenomics #ResearchData - our current focus is on #ROCrate with #JsonLD to describe domain concepts using #MIAPPE, likely #SSN for sensors, #ProvO (or just #ISA) for provenance, public #vocabularies for each partner's measurement types, etc. and lots of image and CSV files.

#MachineLearning is a key use for the #data.

Should we also be looking at #Croissant?

If so, does anyone have experience of combining Croissant and RO-Crate?

Any insights welcomed.

Drones and AI: The New Age of Cotton Production

Researchers develop innovative techniques to track plant aging

Botany One