AgroTools: 一个用于农业中增强工具的多模态代理基准
AgroTools: A Benchmark for Tool-Augmented Multimodal Agents in Agriculture
AI总结：
本文提出AgroTools基准，用于评估农业中增强工具的多模态代理，通过539个问题-答案实例和1097张异构农业图像，评估模型在工具使用中的执行质量和任务成功率。
摘要：
农业决策日益需要能够将视觉观察转化为可靠可执行动作的多模态系统。然而，现有农业多模态基准主要评估最终答案的正确性，并提供有限的支持来评估模型是否能使用外部工具完成高精度工作流。在本文中，我们介绍了AgroTools，一个用于评估农业中增强工具的多模态代理的基准。AgroTools包含539个问题-答案实例和1097张异构农业图像，涵盖五个任务家族和14种农业工具的可执行环境。每个查询都标注了结构化的工具使用轨迹，使能够从两个视角评估执行层面的质量和结果层面的任务成功率。我们对9个开源和4个闭源的多模态大语言模型在AgroTools上进行了基准测试。结果表明，当前模型在农业工具使用场景中仍远未可靠，存在工具规划、论点生成、执行恢复和最终答案综合等方面的明显瓶颈。我们希望AgroTools能支持未来在高精度农业应用中多模态代理的研究。该基准和评估可在https://huggingface.co/datasets/AgroTools/AgroTools上获取。
英文摘要：
Agricultural decision-making increasingly requires multimodal systems that can transform visual observations into reliable, executable actions. However, existing agricultural multimodal benchmarks mainly evaluate final-answer correctness and provide limited support for assessing whether models can use external tools to complete precision-sensitive workflows. In this paper, we introduce AgroTools, a benchmark for evaluating tool-augmented multimodal agents in agriculture. AgroTools contains 539 question-answer instances paired with 1,097 heterogeneous agricultural images, spanning five task families and an executable environment of 14 agricultural tools. Each query is annotated with structured tool-use traces, enabling a dual-view evaluation of both process-level execution quality and outcome-level task success. We benchmark 9 open-source and 4 closed-source multimodal large language models on AgroTools. Results show that current models remain far from reliable in agricultural tool-use settings, with clear bottlenecks in tool planning, argument generation, execution recovery, and final-answer synthesis. We hope AgroTools will support future research on multimodal agents for high-precision agricultural applications. The benchmark and evaluation are available at https://huggingface.co/datasets/AgroTools/AgroTools.
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