Sinusoidal-Illumination Imaging (SII) A Potential Tool for Enhanced Detection of Muscle Defects and Microbial Spoilage of Poultry (2022-2023, USDA-NIFA)

The prevalence of muscle defects in broiler meat, such as white striping, woody breast and spaghetti meat, is threating the profitability and sustainability of the U.S. poultry industry. Poultry products are also highly susceptible to microbial spoilage or contamination, leading to substantial product and economic loss and potential food safety issues. It is thus imperative to develop effective assessment technology to ensure consistent supplies of high-quality poultry products. Current manual assessment systems are labor intensive and prone to human evaluation errors. Computer vision technology is considered as a promising alternative for poultry inspection for being objective, non-destructive, and amenable to automation. But it still falls short of satisfactorily detecting defective or contaminated poultry products. This project aims to develop innovative sinusoidal-illumination imaging (SII) by employing sinusoidal illumination in place of diffuse or uniform illumination that is used by existing imaging technology, for enhanced quality detection of poultry.

Yuzhen Lu
Yuzhen Lu
Assistant Professor

My research interests include optical sensing, machine vision, precision agriculture, food assessment and data analytics.