Yirui Huang

Yirui Huang's work spans applying machine learning, spectroscopy, and imaging techniques to assess fruit and crop quality (such as predicting sugar content in apples or monitoring orchard health via drone-based multispectral imaging), as well as more recent forays into AI methods like graph neural networks, large language models, and Raman spectroscopy for applications including fraud detection and antimicrobial testing. The research combines environmental/agricultural sensing with data-driven analytical tools, though the topic scope is broad and touches materials chemistry and computer science as well. Prospective students would be exposed to interdisciplinary projects mixing remote sensing, spectral data analysis, and modern AI techniques.

Publication output was minimal or absent in the earlier part of the decade but has grown substantially since 2023, with a notable increase in output during 2024-2026.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026