Cotton Weed Database Development and Deep Learning Model Benchmarking ($30,000 for 2021, funded by Cotton Inc., Cary, NC)

This research is to develop a large-scale, public image database of cotton weeds in the field and benchmark deep learning models for weed detection, with the ultimate goal to enhance computer vision-based robotic weeding for cotton. Three sub-objectives are to: 1) Acquire color images in the weed-infested cotton fields under natural light conditions at varied crop growth stages; 2) Expand the image database algorithmically and annotate images for the weed instances at image and pixel levels; 3) Provide benchmarking results of weed classification and detection for the image dataset using deep learning algorithms.

Yuzhen Lu
Yuzhen Lu
Assistant Professor

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