Agricultural and Life Sciences (IFAS)
Artificial Intelligence in Animal Omics Sciences Lab (AIAOS)
Led by Haipeng Yu · Animal Sciences
What we study
The lab uses artificial intelligence and statistical modeling to integrate high-dimensional omics data to improve animal agriculture. They develop AI models and tools to analyze digital phenotypes, genomic data, and other omics data for better animal health and production.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Projects
- 2
- Papers
- 5
Current projects
Heat-Stress Decision Support for Southeast Dairy Farms
The lab is deploying an AI-based platform to improve heat-stress management on dairy farms.
USDA-NIFA Grant to Reduce Antimicrobial Use in Dairy Cattle
The lab is using automation and AI to support dairy cow health and reduce antimicrobial use.
Summaries are written by AI in plain language from public symposium abstracts and may be imperfect.
Recent papers, explained
- Impact of Trait Measurement Error on Quantitative Genetic Analysis of Computer Vision-Derived Traits
2026 · Genes
Shows how computer vision-derived trait errors can mislead genetic studies in pigs.
- Genome-wide association study of the reproductive, body size, and carcass-related latent and directly measured traits in admixed beef heifers
2026 · Frontiers in Genetics
Uses genome-wide studies to compare models for predicting body size, reproduction, and carcass traits in beef heifers.
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- Estimation of Probability of Pregnancy Based on Health Status and Estrus Intensity in Organic Dairy Cows
2026 · Dairy
Links health events and estrus intensity to pregnancy chances in organic dairy cows using predictive models.
- Evaluating transfer learning strategies for improving dairy cattle body weight prediction in a small farm using depth-image and point-cloud data
2026 · Journal of Dairy Science
Tests whether depth images or point-cloud data better predict dairy cattle body weight using transfer learning.
- Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities
2025 · The Plant Phenome Journal
Reviews AI tools for automating plant phenomics and improving crop decision-making.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Haipeng Yu hasn't set up this page yet. Their UF Scholars profile lists their publications.