A knowledge graph for crop diseases and pests in China
Abstract A standardized representation and sharing of crop disease and pest data is crucial for enhancing crop yields, especially in China, which features vast cultivation areas and complex agricultural ecosystems. A knowledge graph for crop diseases and pests, acting as a repository of entities and...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04492-0 |
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| author | Rongen Yan Ping An Xianghao Meng Yakun Li Dongmei Li Fu Xu Depeng Dang |
| author_facet | Rongen Yan Ping An Xianghao Meng Yakun Li Dongmei Li Fu Xu Depeng Dang |
| author_sort | Rongen Yan |
| collection | DOAJ |
| description | Abstract A standardized representation and sharing of crop disease and pest data is crucial for enhancing crop yields, especially in China, which features vast cultivation areas and complex agricultural ecosystems. A knowledge graph for crop diseases and pests, acting as a repository of entities and relationships, is crucial conceptually for achieving unified data management. However, there is currently a lack of knowledge graphs specifically designed for this field. In this paper, we propose CropDP-KG, a knowledge graph for crop diseases and pests in China, which leverages natural language processing techniques to analyze data from the Chinese crop diseases and pests image-text database. CropDP-KG covers relevant information on crop diseases and pests in China, featuring 8 primary entities such as diseases, symptoms, and crops, and is organized into 7 relationships such as primary occurrence locations, affected parts and suitable temperature. In total, it includes 13,840 entities and 21,961 relationships. In the case studies presented in this research, we also show a versatile application of CropDP, namely a knowledge service system, and have released its codebase under an open-source license. The content of this paper provides a guide for users to build their own knowledge graphs, aiming to help them effectively reuse and extend the knowledge graphs they create. |
| format | Article |
| id | doaj-art-2833d845262a42029a023138deac0471 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-2833d845262a42029a023138deac04712025-02-09T12:11:46ZengNature PortfolioScientific Data2052-44632025-02-0112111210.1038/s41597-025-04492-0A knowledge graph for crop diseases and pests in ChinaRongen Yan0Ping An1Xianghao Meng2Yakun Li3Dongmei Li4Fu Xu5Depeng Dang6School of Information Science and Technology, Beijing Forestry UniversitySchool of Information Science and Technology, Beijing Forestry UniversitySchool of Information Science and Technology, Beijing Forestry UniversitySchool of Information Science and Technology, Beijing Forestry UniversitySchool of Information Science and Technology, Beijing Forestry UniversitySchool of Information Science and Technology, Beijing Forestry UniversitySchool of Artificial Intelligence, Beijing Normal UniversityAbstract A standardized representation and sharing of crop disease and pest data is crucial for enhancing crop yields, especially in China, which features vast cultivation areas and complex agricultural ecosystems. A knowledge graph for crop diseases and pests, acting as a repository of entities and relationships, is crucial conceptually for achieving unified data management. However, there is currently a lack of knowledge graphs specifically designed for this field. In this paper, we propose CropDP-KG, a knowledge graph for crop diseases and pests in China, which leverages natural language processing techniques to analyze data from the Chinese crop diseases and pests image-text database. CropDP-KG covers relevant information on crop diseases and pests in China, featuring 8 primary entities such as diseases, symptoms, and crops, and is organized into 7 relationships such as primary occurrence locations, affected parts and suitable temperature. In total, it includes 13,840 entities and 21,961 relationships. In the case studies presented in this research, we also show a versatile application of CropDP, namely a knowledge service system, and have released its codebase under an open-source license. The content of this paper provides a guide for users to build their own knowledge graphs, aiming to help them effectively reuse and extend the knowledge graphs they create.https://doi.org/10.1038/s41597-025-04492-0 |
| spellingShingle | Rongen Yan Ping An Xianghao Meng Yakun Li Dongmei Li Fu Xu Depeng Dang A knowledge graph for crop diseases and pests in China Scientific Data |
| title | A knowledge graph for crop diseases and pests in China |
| title_full | A knowledge graph for crop diseases and pests in China |
| title_fullStr | A knowledge graph for crop diseases and pests in China |
| title_full_unstemmed | A knowledge graph for crop diseases and pests in China |
| title_short | A knowledge graph for crop diseases and pests in China |
| title_sort | knowledge graph for crop diseases and pests in china |
| url | https://doi.org/10.1038/s41597-025-04492-0 |
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