Agricultural and Life Sciences (IFAS)
Yu Lab
Led by Ziwen Yu · Agricultural and Biological Engineering
What we study
The Yu Lab uses data science and AI to improve water management in agriculture, focusing on smart irrigation systems and data collection for small farmers. They develop models to predict soil moisture and explore blockchain for secure data sharing.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Project
- 1
- Papers
- 5
Current projects
USDA CIG project
Models Root Zone Soil Moisture (RZSM) using AI and satellite data to manage irrigation in vegetable farms.
Summaries are written by AI in plain language from public symposium abstracts and may be imperfect.
Recent papers, explained
- Event-Based Decentralized Approach of Root Zone Soil Moisture Increase Dynamics
2026 · Air Soil and Water Research
Models soil moisture increases after water events using a new event-based method.
- An Operation-Centered Review of Deep-Learning Computer Vision for Dairy Cow Management
2026 · Agriculture
Reviews deep learning for automating dairy cow management tasks.
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- Bridging carbon markets and agriculture: a comparative study of stakeholder engagement in South Korea and United States
2025 · Discover Agriculture
Compares how South Korea and the U.S. involve farmers in carbon markets.
- Bridging the gap between water-saving technologies and adoption in vegetable farming: insights from Florida, USA
2025 · Frontiers in Agronomy
Surveys Florida vegetable farmers to understand why they don’t adopt water-saving tech.
- Impact of cross-validation designs on cattle behavior prediction using machine learning and deep learning models with tri-axial accelerometer data
2025 · Smart Agricultural Technology
Tests different ways to split data for predicting cattle behavior using accelerometers.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Ziwen Yu hasn't set up this page yet. Their UF Scholars profile lists their publications.