On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift

• URL: http://arxiv.org/abs/2608.21254v1
• Date: Fri, 21 Aug 2026 16:06:09 GMT
• Title: On the Transferability of Agricultural Weed Detection Under Cross-Field Distribution Shift
• Abstract summary: We characterize where cross-dataset weed-localization performance degrades and which modeling choices recover it.<n>We evaluate the performance of several strategies for transferring a detector trained on one crop to another.
• Score: 13.021915223605681
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 relabel every new deployment field. We introduce a newly collected and annotated UAV image dataset for agricultural weed detection in cotton fields and use it alongside an existing soybean dataset collected under a similar protocol. Using these datasets, we evaluate the performance of several strategies for transferring a detector trained on one crop to another, comparing unsupervised domain adaptive object detection (DAOD) against pretraining on a domain-adjacent source dataset followed by few-shot fine-tuning on the target dataset. Our analysis spans target-domain label budgets from zero to the full target dataset, characterizing the trade-off between adaptation strategy and annotation effort. We find that few-shot fine-tuning with as few as 25 labeled target examples outperforms unsupervised DAOD in our cross-crop comparison, suggesting that source domain selection combined with modest target supervision is more productive than algorithmic sophistication in adaptation.

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