The recent surge in foundation models for natural language processing and computer vision has fueled innovation across various domains. Inspired by this progress, we explore the potential of foundation models for time-series forecasting in smart agriculture, a field often plagued by limited data availability. Specifically, this work presents a novel application of $\texttt{TimeGPT}$, a state-of-the-art (SOTA) time-series foundation model, to predict soil water potential ($\psi_\mathrm{soil}$), a key indicator of field water status that is typically used for irrigation advice. Traditionally, this task relies on a wide array of input variables. We explore $\psi_\mathrm{soil}$'s ability to forecast $\psi_\mathrm{soil}$ in: ($i$) a zero-shot setting, ($ii$) a fine-tuned setting relying solely on historic $\psi_\mathrm{soil}$ measurements, and ($iii$) a fine-tuned setting where we also add exogenous variables to the model. We compare $\texttt{TimeGPT}$'s performance to established SOTA baseline models for forecasting $\psi_\mathrm{soil}$. Our results demonstrate that $\texttt{TimeGPT}$ achieves competitive forecasting accuracy using only historical $\psi_\mathrm{soil}$ data, highlighting its remarkable potential for agricultural applications. This research paves the way for foundation time-series models for sustainable development in agriculture by enabling forecasting tasks that were traditionally reliant on extensive data collection and domain expertise.

翻译：近期，自然语言处理和计算机视觉领域基础模型的蓬勃发展推动了各领域的创新。受此进展启发，我们探索了基础模型在智慧农业时序预测中的应用潜力，该领域常受数据可用性有限的困扰。具体而言，本研究首次将最先进的时序基础模型 $\texttt{TimeGPT}$ 应用于土壤水势（$\psi_\mathrm{soil}$）预测，该指标是田间水分状况的关键表征，通常用于灌溉决策。传统上，此任务依赖于大量输入变量。我们探究了 $\texttt{TimeGPT}$ 在以下三种场景中预测 $\psi_\mathrm{soil}$ 的能力：（$i$）零样本场景，（$ii$）仅依赖历史 $\psi_\mathrm{soil}$ 测量数据进行微调的场景，以及（$iii$）在模型中引入外生变量后进行微调的场景。我们将 $\texttt{TimeGPT}$ 的性能与预测 $\psi_\mathrm{soil}$ 的现有最先进基线模型进行了比较。结果表明，$\texttt{TimeGPT}$ 仅利用历史 $\psi_\mathrm{soil}$ 数据即可获得具有竞争力的预测精度，彰显了其在农业应用中的巨大潜力。本研究通过实现传统上依赖大量数据收集和领域专业知识的预测任务，为农业可持续发展中的时序基础模型应用铺平了道路。