Sorghum Panicle Detection
Designing Computer Vision Models for Detecting Sorghum Panicles in Large Field Scans
Collaborators: Pauli Lab, University of Queensland
- Architectured CNNs for crop-level panicle detection on Field Scanner Data
- Increased testing speeds by 30% via developing a randomized sample Python script
- Led the team effort of manual test data labeling for over 400 sample scans in a week