Understanding spatial distribution of fallow land is important for optimizing the food-water (FW) nexus, given fallowing's role in crop rotation and water conservation. Fallow is a low accuracy class in USDA Cropland Data Layer (CDL). Geospatial foundation model (GFM), Prithvi-EO has shown strong transferability across computer vision tasks. However, its Vision Transformer (ViT) backbone produces features at a single spatial scale that are ill-suited for the multi-scale features required by object detection heads. Existing approaches synthesise multi-scale pyramids through scaling of single stride tokens, sacrificing spatial heterogeneity, and full backbone fine-tuning is computationally prohibitive for GFMs. We evaluate a fallow detection pipeline combining two parameter-efficient fine tuning (PEFT) schemes: Low-Rank Adaptation (LoRA) and a hybrid PEFT, with three neck designs: pseudo multi-scale, Lite ViT-Adapter, and Full ViT-Adapter. Our best configuration, Lite ViT-Adapter with a one-stage head, achieves a mAP@50 of 0.9479 with the Diou loss, suggesting the effectiveness of center-aware localization for irregular fallow field detection. ViT-Adapter free one-stage detection under LoRA improves the adapter-free anchor-based approach by 6.42%, and the best configuration improves baseline adapter-free anchor-based approach by 25.70%. These results demonstrate that lightweight spatial prior fusion and selective backbone unfreezing enable Prithvi-EO to capture local fallow patterns more effectively, outperforming approaches that rely on reshaped single-stride ViT tokens.

翻译：理解休耕地的空间分布对于优化粮食-水耦合关系至关重要，因为休耕在作物轮作和水资源保护中发挥着关键作用。在美国农业部作物数据层中，休耕是精度较低的类别。地理空间基础模型Prithvi-EO在计算机视觉任务中展现出强大的迁移能力。然而，其视觉Transformer骨干网络仅在单一空间尺度上产生特征，难以满足目标检测头所需的多尺度特征。现有方法通过对单步长词元进行缩放来合成多尺度金字塔，这牺牲了空间异质性，而完全微调骨干网络对地理空间基础模型而言计算成本过高。我们评估了一种结合两种参数高效微调方案（低秩适配和混合参数高效微调）与三种颈部设计（伪多尺度、轻量级ViT适配器和全量ViT适配器）的休耕地检测流水线。最佳配置（采用单阶段检测头的轻量级ViT适配器）在DIoU损失函数下达到了0.9479的mAP@50，表明中心感知定位对不规则休耕地检测的有效性。基于LoRA的免ViT适配器单阶段检测相比免适配器的锚点法提升了6.42%，而最佳配置相比基线免适配器锚点法提升了25.70%。这些结果表明，轻量级空间先验融合与选择性骨干网络解冻能够使Prithvi-EO更有效地捕捉局部休耕模式，优于依赖重塑单步长ViT词元的方法。