基础模型与农业：预训练之外的挑战

中文摘要 AI 辅助

全球粮食安全和可持续气候行动越来越依赖可靠、可扩展的农业监测。地球观测基础模型已成为通用遥感领域强大且标签高效的工具，但早期将其部署到农业应用的尝试却取得了差得出乎意料的结果。我们假设这种性能差距源于农业景观的极端异质性，以及当前地球观测基础模型无法适配任务特定细微差别的固有缺陷。本研究系统评估了阻碍基础模型部署到农业任务的两个关键瓶颈，在七个真实农业数据集上对两款地球观测基础模型、一款为表格数据设计的基础模型以及传统监督基线进行了基准测试，这些数据集涵盖产量预测、物候估计和作物分类。首先，我们发现了预训练-部署模态差距：农业下游任务常需要地球观测基础模型架构无法适配的多种非影像数据模态，而专为表格数据设计的基础模型能更自然地处理这种异质性。其次，我们通过五个结构轴明确了农业任务空间，以证明当前模型为何无法可靠泛化，导致不同评估设置下模型排名高度不稳定。通过表征这些结构和模态差距，我们的见解凸显了通用架构与专门农业下游数据之间的摩擦，为开发下一代领域感知基础模型提供了战略路线图。

英文摘要

Global food security and sustainable climate action increasingly rely on robust, scalable agricultural monitoring. Earth observation foundation models have emerged as powerful, label-efficient tools across general remote sensing domains, yet early attempts to deploy them for agricultural applications have yielded surprisingly poor results. We hypothesize that this performance gap stems from the extreme heterogeneity of agricultural landscapes and the inherent inability of current earth observation foundation models to adapt to task-specific nuances. In this work, we systematically evaluate two critical bottlenecks hindering the deployment of foundation models in agricultural tasks, benchmarking two earth observation foundation models, a foundation model designed for tabular data, and conventional supervised baselines across seven real-world agricultural datasets spanning yield prediction, phenology estimation, and crop classification. First, we identify a pretraining-deployment modality gap: agricultural downstream tasks frequently require diverse, non-imagery data modalities that earth observation foundation models are architecturally unequipped to ingest, while a foundation model built for tabular data handles this heterogeneity more naturally. Second, we formalize the agricultural task space across five structural axes to demonstrate why current models fail to generalize reliably, resulting in highly unstable model rankings across evaluation settings. By characterizing these structural and modal gaps, our insights highlight the friction between general-purpose architectures and specialized agricultural downstream data, providing a strategic roadmap for developing the next generation of domain-aware foundation models.

发表机构

• Wageningen University and Research（瓦赫宁根大学与研究中心）
• Zhejiang Academy of Agricultural Sciences（浙江省农业科学院）
• University of Bonn（波恩大学）

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