PlantExpertVQA: 一个用于植物科学中视觉语言模型基准测试的视觉问答数据集
PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science
AI总结：
PlantExpertVQA数据集旨在提升视觉语言模型在农业决策中的应用，包含765,186个高质量问答对，涵盖38种作物和89种病害，通过多阶段流程生成并经专家审核，验证了参数高效微调的有效性。
摘要：
现有植物疾病数据集主要针对分类和检测，使视觉语言模型无法支持交互式、基于推理的诊断。为此，我们提出了PlantExpertVQA，一个大规模的视觉问答（VQA）数据集，旨在推进视觉语言模型在农业决策中的应用。该数据集由45个开源数据集组成，包括广泛使用的PlantVillage语料库，包含765,186个高质量问题-答案（QA）对，基于150,841张图像，涵盖38种作物物种和89种疾病条件。问题按认知复杂性分为3级，并分为9个不同的类别。每个问题均按照专家指导制定，并通过自动化两阶段流程生成：基于图像元数据的模板式QA合成，随后是多阶段语言重工程。数据集经过迭代审核，由领域专家确保科学准确性和相关性。我们发现，当前前沿的视觉语言模型，包括最近的开源指令调优多模态大语言模型，在PlantExpertVQA上表现不佳。然而，对紧凑型2B参数模型在数据集小部分上的参数高效微调，在所有问题类别上均取得显著提升，证明了其在领域适应中的有效性。
英文摘要：
Existing plant-disease datasets target classification and detection, leaving vision-language models unable to support interactive, reasoning-based diagnosis. To address this, we present PlantExpertVQA, a large-scale visual question answering (VQA) dataset designed to advance vision-language models for agricultural decision-making. It is compiled from 45 open-source datasets, including the widely used PlantVillage corpus, and comprises 765,186 high-quality question-answer (QA) pairs grounded over 150,841 images spanning 38 crop species and 89 disease conditions. Questions are organized into 3 levels of cognitive complexity and 9 distinct categories. Each was phrased following expert guidance and generated via an automated two-stage pipeline: template-based QA synthesis from image metadata, followed by multi-stage linguistic re-engineering. The dataset was iteratively reviewed by domain experts for scientific accuracy and relevance. We find that current frontier vision-language models, including recent open-source instruction-tuned multimodal LLMs, perform poorly on PlantExpertVQA. However, parameter-efficient fine-tuning of a compact 2B-parameter model on a small fraction of the dataset yields substantial improvements across all question categories, demonstrating its effectiveness for domain adaptation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
