LeafNet：一个大规模数据集和全面基准，用于植物病害的基础视觉-语言理解
LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseases
AI总结：
LeafNet通过大规模多模态数据集和基准测试，揭示了VLMs在植物疾病理解中的性能差异，强调了多模态架构在提升诊断精度中的关键作用。
摘要：
基础模型和视觉-语言预训练显著推动了视觉-语言模型（VLMs）的发展，使其能够多模态处理视觉和语言数据。然而，其在特定领域农业任务中的应用，如植物病理学，仍受到缺乏大规模、全面的多模态图像-文本数据集和基准的限制。为解决这一差距，我们引入了LeafNet，一个全面的多模态数据集，以及LeafBench，一个视觉问答基准，旨在系统评估VLMs在理解植物疾病方面的能力。该数据集包含186,000张叶片数字图像，涵盖97种疾病类别，配以元数据，生成13,950个问题-答案对，涵盖六个关键农业任务。问题评估了植物病理学理解的各个方面，包括视觉症状识别、分类学关系和诊断推理。在我们的LeafBench数据集上对12种最先进的VLMs进行基准测试，我们揭示了它们在疾病理解能力上的显著差异。我们的研究显示，性能在不同任务中变化明显：二分类健康-疾病分类超过90%的准确率，而细粒度病原体和物种识别仍低于65%。直接比较视觉模型和VLMs显示了多模态架构的关键优势：微调的VLMs优于传统视觉模型，证实了整合语言表示显著提高了诊断精度。这些发现突显了当前VLMs在植物病理学应用中的关键差距，并强调了LeafBench作为严格框架的重要性，以促进方法学进步和向可靠AI辅助植物疾病诊断的进展评估。代码可在https://github.com/EnalisUs/LeafBench获取。
英文摘要：
Foundation models and vision-language pre-training have significantly advanced Vision-Language Models (VLMs), enabling multimodal processing of visual and linguistic data. However, their application in domain-specific agricultural tasks, such as plant pathology, remains limited due to the lack of large-scale, comprehensive multimodal image--text datasets and benchmarks. To address this gap, we introduce LeafNet, a comprehensive multimodal dataset, and LeafBench, a visual question-answering benchmark developed to systematically evaluate the capabilities of VLMs in understanding plant diseases. The dataset comprises 186,000 leaf digital images spanning 97 disease classes, paired with metadata, generating 13,950 question-answer pairs spanning six critical agricultural tasks. The questions assess various aspects of plant pathology understanding, including visual symptom recognition, taxonomic relationships, and diagnostic reasoning. Benchmarking 12 state-of-the-art VLMs on our LeafBench dataset, we reveal substantial disparity in their disease understanding capabilities. Our study shows performance varies markedly across tasks: binary healthy--diseased classification exceeds 90% accuracy, while fine-grained pathogen and species identification remains below 65%. Direct comparison between vision-only models and VLMs demonstrates the critical advantage of multimodal architectures: fine-tuned VLMs outperform traditional vision models, confirming that integrating linguistic representations significantly enhances diagnostic precision. These findings highlight critical gaps in current VLMs for plant pathology applications and underscore the need for LeafBench as a rigorous framework for methodological advancement and progress evaluation toward reliable AI-assisted plant disease diagnosis. Code is available at https://github.com/EnalisUs/LeafBench.
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。
