In precision agriculture, vision models often struggle with new, unseen fields where crops and weeds have been influenced by external factors, resulting in compositions and appearances that differ from the learned distribution. This paper aims to adapt to specific fields at low cost using Unsupervised Domain Adaptation (UDA). We explore a novel domain shift from a diverse, large pool of internet-sourced data to a small set of data collected by a robot at specific locations, minimizing the need for extensive on-field data collection. Additionally, we introduce a novel module -- the Multi-level Attention-based Adversarial Discriminator (MAAD) -- which can be integrated at the feature extractor level of any detection model. In this study, we incorporate MAAD with CenterNet to simultaneously detect leaf, stem, and vein instances. Our results show significant performance improvements in the unlabeled target domain compared to baseline models, with a 7.5% increase in object detection accuracy and a 5.1% improvement in keypoint detection.

本研究解决了精细农业中视觉模型在新领域中面临的适应性挑战，特别是当作物和杂草因外部因素影响出现组成和外观差异时。我们提出了一种低成本的无监督领域适应方法，利用网络来源的数据与机器人在特定地点收集的小型数据集之间的领域转换，同时引入了多级注意力对抗判别模块（MAAD），显著提高了在未标记目标领域内的对象检测精度和关键点检测效果。

从网络数据到实际场景：农业机器人低成本无监督领域适应