这篇论文研究了农业杂草检测的迁移性，比较了不同迁移策略，发现少量样本微调效果显著，对于需要跨作物迁移检测器的农业应用有重要意义。
本研究评估了将训练于一种作物的杂草检测器迁移到另一种作物的性能，比较了无监督域自适应目标检测与预训练和少量样本微调方法。研究发现，少量样本微调在跨作物比较中优于无监督域自适应目标检测，表明源域选择与适度目标监督比算法复杂性更有效。
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 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.