Journal of Agricultural Big Data ›› 2025, Vol. 7 ›› Issue (3): 371-378.doi: 10.19788/j.issn.2096-6369.100060

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Image-Text Multi-Modal Dataset of Corn Leaf Diseases based on Manual Annotation and Contrast Generation Model

WANG YanFang1,2(), XIAN GuoJian1,2,3, ZHAO RuiXue1,2,3,*()   

  1. 1. Agricultural Information Institute of CAAS, Beijing 100081, China
    2. Key Laboratory of Knowledge Mining and Knowledge Services in Agricultural Converging Publishing, National Press and Publication Administration, Beijing 100081, China
    3. Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
  • Received:2025-04-16 Accepted:2025-05-20 Online:2025-09-26 Published:2025-09-28
  • Contact: ZHAO RuiXue

Abstract:

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:

Item Description
Dataset name Image-Text Multi-Modal Dataset of Corn Leaf Diseases based on Manual Annotation and Contrast Generation Model
Specific subject area Agricultural Science, Computer Science
Research topic Computer vision, Cross-modal retrieval, Image captioning
Data types and technical formats .jpg
Dataset structure The dataset is composed of two parts: image data of corn leaf disease and corresponding text description data, including: 1. the original image data set of leaf disease, including 9 typical disease image data, including large leaf spot, small leaf spot, brown spot, Curvularia leaf spot, common rust, southern rust, gray spot, round spot and dwarf mosaic, with a total of 1653 pieces; 2. the diagnostic text description corresponding to the image has an average length of about 32 characters, a total of 1653.
Volume of dataset 3.87 GB
Key index in dataset Image and its corresponding description text
Data accessibility CSTR:17058.11.sciencedb.agriculture.00226; https://cstr.cn/17058.11.sciencedb.agriculture.00226
DOI:10.57760/sciencedb.agriculture.00226; https://doi.org/10.57760/sciencedb.agriculture.00226
Financial support National Science and Technology Major Project(2021ZD0113705).

Key words: corn, leaf diseases, multimodal data set, image description, automatic annotation