TomaMMU：用于番茄叶片病害的综合多模态理解基准
TomaMMU: A Comprehensive Multimodal Understanding Benchmark for Tomato Leaf Diseases
- FPT University（FPT大学）
- Korea University（高丽大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究推出用于番茄叶片病害多模态理解的TomaMMU数据集及TomaBench基准，评估14种VLMs时发现其细粒度识别等存在差距，微调后MCQ准确率达96.09%，为相关研究提供了方向。
AI中文摘要：
为解决这一空白，我们推出了TomaMMU，这是一个大规模的番茄叶片病害多模态理解数据集，同时还推出了TomaBench，这是一个用于评估视觉语言模型（VLMs）在番茄病害理解方面性能的基准。TomaMMU包含28808张高质量图像，涵盖15个类别，以及213119个人工标注的视觉问答对，这些问答对是通过包含数据收集、人工标注和问答生成三个阶段的流程生成的。基于此基础，TomaBench将7项农业任务组织成一个分层三级分类体系，涵盖基础感知、病理学理解和专家诊断，共同实现从低级视觉识别到高级诊断推理的系统评估。这些任务评估视觉症状识别、分类关系和诊断推理，全面展现模型对植物病理学的掌握程度。我们对14种最先进的VLMs的研究结果显示，它们在细粒度识别和基于事实的推理方面存在显著差距，在具有挑战性的多项选择题（MCQs）和开放式问题上的表现始终不佳。这些结果表明，当前的VLMs难以将视觉感知转化为可靠的诊断知识，因此需要针对性的领域适配。在TomaMMU上进行简单的微调就大幅缩小了这一差距，使具有挑战性的MCQs的准确率提升至96.09%，优于近期的VLMs，为未来工作指明了有前景的方向。所有数据和代码可在该https URL获取。
英文摘要：
To address this gap, we introduce TomaMMU, a large-scale Tomato leaf disease MultiModal Understanding dataset, alongside TomaBench, a benchmark for evaluating VLMs on tomato disease understanding. TomaMMU comprises 28,808 high-quality images spanning 15 categories and 213,119 human-annotated visual question-answer pairs, generated through a three-stage pipeline comprising Data Collection, Human Annotation, and Question-Answer Generation. Building on this foundation, TomaBench organizes seven agricultural tasks into a hierarchical three-level taxonomy spanning Basic Perception, Pathology Understanding, and Expert Diagnosis, which together enable systematic evaluation from low-level visual recognition to high-level diagnostic reasoning. The tasks assess visual symptom recognition, taxonomic relationships, and diagnostic reasoning, offering a comprehensive view of how well models grasp plant pathology. Our results pronounced gaps in fine-grained recognition and factually grounded reasoning with 14 state-of-the-art VLMs, consistently underperforming on both challenging MCQs and open-ended questions. These results suggest that current VLMs struggle to translate visual perception into reliable diagnostic knowledge, motivating the need for targeted domain adaptation. Simple fine-tuning on TomaMMU substantially narrows this gap, boosting accuracy on challenging MCQs to 96.09%, outperforming recent VLMs, and pointing toward promising directions for future work. All data and code is available in https://huggingface.co/datasets/enalis/TomaMMU.
