首个面向农业的多模态大模型评测基准，解决农业知识数据匮乏问题。
AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models
- 基于LUCAS数据构建包含1784张遥感图像的多模态农业数据集
- 涵盖地理、土地利用等详细标注，支持农业场景理解与分析
- 适合农业AI研究者和多模态模型开发者使用
我们提出AgriBench，首个专为农业应用设计的多模态大语言模型（MM-LLMs）评测基准。为解决农业知识数据集稀缺的问题，我们构建了MM-LUCAS多模态农业数据集，包含1,784张景观图像、分割掩码、深度图及详细标注（地理坐标、国家、日期、土地覆盖与土地利用分类信息、质量评分、美学评分等），其基础数据源自欧洲联盟（EU）的《土地利用/覆被面积调查》（LUCAS）项目。该工作为推进农业领域多模态大模型的发展提供了开创性视角，目前仍在持续完善，可为未来专家知识驱动的多模态模型研究提供重要参考。
原文摘要 · Abstract (English)
We introduce AgriBench, the first agriculture benchmark designed to evaluate MultiModal Large Language Models (MM-LLMs) for agriculture applications. To further address the agriculture knowledge-based dataset limitation problem, we propose MM-LUCAS, a multimodal agriculture dataset, that includes 1,784 landscape images, segmentation masks, depth maps, and detailed annotations (geographical location, country, date, land cover and land use taxonomic details, quality scores, aesthetic scores, etc), based on the Land Use/Cover Area Frame Survey (LUCAS) dataset, which contains comparable statistics on land use and land cover for the European Union (EU) territory. This work presents a groundbreaking perspective in advancing agriculture MM-LLMs and is still in progress, offering valuable insights for future developments and innovations in specific expert knowledge-based MM-LLMs.
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