Agricultural and Life Sciences (IFAS)
Jarquín Lab
Led by Diego Jarquín · Agronomy
What we study
The Jarquín Lab uses artificial intelligence and multi-omics data to improve plant breeding, focusing on developing tools to analyze large datasets and uncover biological insights for more productive and resilient crops.
What working here is like
- Computational
- Plants
- Coding required
- Papers
- 5
Recent papers, explained
- Improving grain yield prediction in Southern US oat germplasm using genomics information and environmental covariates
2026 · The Plant Genome
Compares deep learning and other models for predicting oat grain yield.
- Prediction of conelet mortality rates in interior lodgepole pine using phenomic, nutritional, and environmental data
2026 · Frontiers in Plant Science
Explores using plant data to predict conelet survival in lodgepole pine.
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- Complex and Non-Linear Modeling of Genotype × Environment Interaction via Tree-Based Learning Algorithms and Shapley Decomposition
2026 · Agriculture
Uses machine learning to model complex interactions between maize genotypes and environments.
- Development of conditional cores via supersaturated designs for genetic diversity–based prediction models
2026 · The Plant Genome
Uses a method to pick the best soybean lines for training genetic prediction models.
- Reaction norm modeling of high‐dimensional genomic and environmental data improves prediction accuracy in winter wheat
2026 · The Plant Genome
Tests whether adding weather data to genomic models improves predictions for winter wheat traits.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Diego Jarquín hasn't set up this page yet. You can also look them up in the UF directory (search “Diego Jarquín”).