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: