On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift
Accurate agricultural weed detection in real-world field conditions is essential for precision agriculture, enabling targeted intervention and reducing yield loss. Recent work has reported strong detection performance from UAV-based imagery across a range of crops, yet existing approaches evaluate within a single crop and field, leaving practitioners with little evidence that a model trained on one crop will generalize to a new field or crop type. In this work, we characterize where cross-dataset weed-localization performance degrades and which modeling choices recover it, reducing the need to
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%halfrost/Halfrost-Field →
“Fuzzy title match (0.73): “On the Transferability of Agricultural Weed Detection Under ” ≈ “halfrost/Halfrost-Field””
- LinkedLinked via arxiv author · 85%Nikhilesh Prabhakar →
“On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift”
- LinkedLinked via arxiv author · 85%Pranuthi Tenali →
“On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift”
- LinkedLinked via arxiv author · 85%Wilfredo Abudeye Fernandez →
“On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift”
- LinkedLinked via arxiv author · 85%Shekhar Borah →
“On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift”
- LinkedLinked via arxiv author · 85%Athresh Karanam →
“On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift”
- LinkedLinked via arxiv author · 85%Erik Blasch →
“On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift”
- LinkedLinked via arxiv author · 85%Prabha Sundaravadivel →
“On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift”