评估农业潜力是可持续土地管理与农业规划的基础。遥感数据正日益成为评估农业潜力的重要手段，因其相较传统方法（如实地调查、原位测量、土壤检测等）更具成本效益。ImageCLEF AI4Agri 2026挑战赛子任务1聚焦于法国南部葡萄栽培潜力的预测。DS@GT ARC团队在该子任务中的参赛方案提出了一种U-Net与地理空间基础模型（Prithvi-2.0）的集成方法。本方案最优模型在排行榜上达到±1精度68.32，位列7支参赛队伍中的第2名。本工作的实现代码已公开发布于https://github.com/dsgt-arc/imageclef-ai4agri-2026。

Determining agricultural potential is fundamental to sustainable land management and agricultural planning. Remote sensing data is increasingly valuable as an avenue for agricultural potential due to the cost of traditional methods (surveys, in-situ measurements, soil testing, etc). ImageCLEF AI4Agri 2026: Subtask 1 is concerned with the prediction of viticulture potential in Southern France. The DS@GT ARC's submission for Subtask 1 introduces an ensemble of U-Net and a Geospatial Foundation Model (Prithvi-2.0). Our best model achieved a $\pm$1 accuracy of 68.32 on the leaderboard, ranking 2nd among 7 teams. The implementation for this work is publicly available at https://github.com/dsgt-arc/imageclef-ai4agri-2026 .