玉米叶部病害的精准识别是农业智能化管理的重要环节。现有玉米病害数据集存在质量参差不齐、标签类别模糊、多模态数据匮乏等问题，尤其是中文语境下的病害描述数据的稀缺性。本研究整合了自建数据与AI Challenger、Plant Village及OpenDataLab开源的玉米叶部病害高清图像数据，并由人工基于文献、专业书籍及科学数据等先验知识对图像进行诊断性文本描述标注，共构建了中文语境下的1 653组图像-文本对多模态数据集。其中，每张图像对应的文本模态内容涵盖病害类型、病状特征及严重程度等关键信息。在此基础上，探索使用CN-CLIP与GPT2-Chinese大模型组合生成图像描述的补充增强内容，丰富描述文本模态数据的多样性，为图像自动标注提供实践验证。本数据集可为玉米病害智能诊断模型开发、中文图像描述生成及农业多模态知识图谱构建提供高质量数据样本支撑。

数据摘要：

Accurately identifying corn leaf diseases is an important part of intelligent agricultural management. The existing maize disease data sets have problems such as uneven quality, fuzzy label categories, and lack of multimodal data, especially the scarcity of disease description data in the Chinese context. This data set integrates the image data of corn disease from open source platforms such as AI Challenger, PlantVillage and OpenDataLab, and complements the high-definition disease images collected in the field. A Chinese multimodal data set containing 1653 images is constructed. Each image has its corresponding diagnostic text description, covering key information such as disease type, disease characteristics and severity. At the same time, the cn-clip and CPT2 Chinese large model are combined to achieve image description generation, which provides a method for automatic annotation. This data set can provide high-quality data support for the development of an intelligent diagnosis model of corn disease, the generation of Chinese image description and the construction of an agricultural multimodal knowledge map.

Data summary: