Research on Light-Resilient Ripeness Detection for Strawberries Published

A paper led by PhD student Meili Sun, titled “Light-resilient visual regression of strawberry ripeness for robotic harvesting,” has been accepted for publication in Computers and Electronics in Agriculture.

The research introduces a robust visual regression model that maintains high accuracy in judging strawberry ripeness under varying greenhouse light conditions. This technology is key for enabling consistent, selective harvesting by robots.

Congratulations to the authors!