评估现有大模型在农业任务中的表现，提出需专为农业设计的专用大模型。
From General to Specialized: The Need for Foundational Models in Agriculture
- 构建农业大模型需求框架，评估通用大模型适配性
- 在作物类型识别等三项任务中验证通用模型表现不足
- 呼吁开发专门面向农业的专用基础模型
粮食安全在全球人口增长和气候变化加剧背景下仍面临严峻挑战，亟需可持续农业生产力的创新解决方案。近年来，基础模型在遥感与气候科学领域展现出卓越性能，为农业监测带来新机遇。然而，其在作物类型识别、作物物候估计和产量预测等农业核心任务中的应用仍不充分。本文定量评估现有基础模型在代表性农业任务中的有效性，从农业领域视角提出理想农业基础模型（CropFM）的需求框架，并在此框架下调研比较现有通用基础模型，实证评估其中两个典型模型在三项农业特定任务中的表现。结果表明当前通用模型存在局限，亟需开发专为农业定制的基础模型。
原文摘要 · Abstract (English)
Food security remains a global concern as population grows and climate change intensifies, demanding innovative solutions for sustainable agricultural productivity. Recent advances in foundation models have demonstrated remarkable performance in remote sensing and climate sciences, and therefore offer new opportunities for agricultural monitoring. However, their application in challenges related to agriculture-such as crop type mapping, crop phenology estimation, and crop yield estimation-remains under-explored. In this work, we quantitatively evaluate existing foundational models to assess their effectivity for a representative set of agricultural tasks. From an agricultural domain perspective, we describe a requirements framework for an ideal agricultural foundation model (CropFM). We then survey and compare existing general-purpose foundational models in this framework and empirically evaluate two exemplary of them in three representative agriculture specific tasks. Finally, we highlight the need for a dedicated foundational model tailored specifically to agriculture.
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