Ph.D. Student, Mechanical Engineering
Joined Spring 2022
- B.E., Agricultural Engineering, Federal University of Technology, Akure, Nigeria, 2019
- Translational AI Center (TrAC), Iowa State University
Timilehin Ayanlade is a Ph.D. student in Mechanical Engineering. He joined the Self-aware Complex Systems Laboratory, part of the Translational AI Center at Iowa State University, in Spring 2022. His research focuses on machine learning for multimodal understanding in cyber-agricultural systems, including the deployment of machine learning models in high-throughput agricultural pipelines and the robustness, explainability, and trustworthiness of deployed models. He also enjoys playing chess, reading books, and public speaking.
Research interests: Machine learning, multimodal learning for cyber-agricultural systems, computer vision, robust and explainable AI
Publications with the lab (9)
- TabQL: In-Context Q-Learning with Tabular Foundation Models — arXiv (2026)
- Transformer model to determine spatio-temporal relationships of variables, and interpretability for soybean seed yield, oil, and protein prediction — Frontiers in Artificial Intelligence (2026)
- WeedNet: a foundation model-based global-to-local AI approach for weed species identification and classification — Nature Communications (2026)
Show all 9
- Use of artificial intelligence in soybean breeding and production — Advances in Agronomy 190 (2025)
- Plot‐level satellite imagery can substitute for UAVs in assessing maize phenotypes across multistate field trials — Plants, People, Planet (2025)
- Assessing phenotypic diversity and sensor‐based metrics for drought response in soybean — Crop Science (2025)
- Robust soybean seed yield estimation using high-throughput ground robot videos — Frontiers in Plant Science (2025)
- Multi-modal AI for ultra-precision agriculture — Harnessing Data Science for Sustainable Agriculture and Natural Resource … (2024)
- Multi‐sensor and multi‐temporal high‐throughput phenotyping for monitoring and early detection of water‐limiting stress in soybean — The Plant Phenome Journal (2024)
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