In-field high-throughput phenotyping of short-stature crops
We are currently investigating computer vision and deep learning algorithms to help plant scientists quantify plant architecture of peanut, cotton and soybean under field conditions. We will develop autonomous mobile platforms for the 2020 growing season.
Automated equine gait analysis using 3D computer vision and deep learning
We are working on automated detection and tracking of horse pose in videos. The outcome of this research will enable animal scientists to perform gait analysis for a large, diverse horse population.
High-throughput phenotyping of pine tree architecture
We are investigating 3D stereo vision and deep convolutional neural networks for morphological characterization of loblolly pine.
Some interesting work in the past