Research on plant perception, occlusion, and multimodal learning.
Research in progress
This project investigates recovery of occluded plant leaves using RGB-D observations and an identity-matched history across days. The methodology separates mask reconstruction from reliability estimation. Work includes correspondence auditing, independent evaluation, and controlled ablations.
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TAHAF uses trait-adaptive hierarchical attention fusion to combine RGB and depth information for lettuce phenotyping in hydroponic systems.
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CLFM explores label-efficient plant trait prediction by bringing greenhouse images and climate measurements into a shared learning framework. Current development investigates image-environment contrastive learning and adaptation of vision backbones.
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