跨田间分布偏移下的农业杂草检测可迁移性研究

中文摘要 AI 辅助

在实际田间条件下实现精准的农业杂草检测对精准农业至关重要，可支持靶向干预并减少产量损失。近期研究表明，基于无人机（UAV）的图像在多种作物上可实现优异的杂草检测性能，但现有方法仅在单一作物和单一田间内进行评估，使得从业者缺乏证据证明在某一作物上训练的模型能泛化到新的田间或作物类型。本研究明确了跨数据集杂草定位性能下降的场景，以及哪些建模选择可恢复该性能，从而减少对每个新部署田间重新标注的需求。我们引入了新收集并标注的棉花田农业杂草检测无人机图像数据集，并将其与采用相似协议收集的现有大豆数据集结合使用。利用这些数据集，我们评估了多种将在某一作物上训练的检测器迁移到另一作物的策略，比较了无监督域自适应目标检测（DAOD）与在域邻近源数据集上预训练后在目标数据集上进行少样本微调的方法。我们的分析覆盖了目标域标签预算从0到完整目标数据集的范围，明确了自适应策略与标注工作量之间的权衡。研究发现，在跨作物比较中，仅使用25个标注目标样本的少样本微调性能优于无监督DAOD，这表明源域选择结合适度的目标监督比自适应算法的复杂度更有效。

英文摘要

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.

发表机构

• The University of Texas at Dallas（德克萨斯大学达拉斯分校）
• The University of Texas at Tyler（德克萨斯大学泰勒分校）
• Air Force Research Lab（空军研究实验室）

机构由 AI 辅助整理，请以论文原文为准。