Agricultural and Life Sciences (IFAS)
Xu (Kevin) Wang Lab
Led by Xu Wang · Agricultural and Biological Engineering
What we study
The Wang Lab uses AI and machine vision to study plant growth, focusing on wheat and specialty crops. They develop digital twin technologies to improve breeding efficiency.
What working here is like
- Computational
- Fieldwork
- Building & testing
- Plants
- Coding required
- Project
- 1
- Papers
- 5
Current projects
AI-powered machine vision
Developing tools to analyze plant growth using artificial intelligence.
Summaries are written by AI in plain language from public symposium abstracts and may be imperfect.
Recent papers, explained
- Identifying Agricultural Injuries Requiring Trauma Care in Florida
2026 · Safety
Identifies agricultural injuries in Florida using trauma registry records and ICD-10 codes.
- UAV-based 3D canopy reconstruction for non-destructive strawberry biomass estimation: Comparison of SfM, NeRF, and Gaussian Splatting
2026 · Smart Agricultural Technology
Compares drone and ground-based methods to estimate strawberry plant volume as a biomass proxy.
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- Exploring the efficacy of phenomic and genomic selection for yield and fruit quality traits in strawberry
2025 · The Plant Genome
Compares genomic and phenomic selection for strawberry yield and quality.
- Predictions of wheat phenotypic variability by integrating high-throughput phenotyping observations into a crop growth model
2025 · Plant Phenomics
Uses wheat growth model and drone images to predict crop performance faster.
- Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities
2025 · The Plant Phenome Journal
Reviews how AI can improve plant phenomics by automating sensing and data analysis.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Xu Wang hasn't set up this page yet. You can also look them up in the UF directory (search “Xu Wang”).