A dataset of entity and relation recognition of China’s major grain varieties in 2023

<p indent="0mm">With the continuous growth of the world population and the development of science and technology, the combination of agriculture and advanced scientific technology is characterized by sustainability, digitization, and precision production. In this development process, the leverage of large-scale, high-dimensional, structured, semi-structured, and unstructured agricultural data generated over time has become a big challenge in guiding agricultural breeding and production. To address this challenge, we constructed a dataset of crop-centric entity and relation recognition for major grain crops, aiming to support breeding and production activities more effectively. Firstly, domain-specific literature was collected and processed for major grain crops such as rice, corn, and barley. Secondly, based on domain knowledge from the National Agricultural Science Data Center and plant ontology databases, we established a set of crop-specific entity relation classification standards. Finally, the dataset was supplemented with manually annotated data. The dataset includes entity recognition data for three main grain crops and relation recognition data, with a total of 33306 entity recognition records and 25846 relation recognition records, amounting to 4.58MB. The dataset has undergone professional data management and was reviewed and classified by agricultural experts through professional data management, ensuring its high quality and reliability. The dataset provides fundamental data for entity and relation recognition of main grain crops, which is beneficial for promoting the cultivation and production of main grain crops and holds significant practical application value.

Authors

Publication Details

Journal
China Scientific Data
Published
2025-05-29
DOI
https://doi.org/10.11922/11-6035.nasdc.2023.0035.zh
Primary Topic
Remote Sensing and Land Use
Type
article
Field-Weighted Citation Impact
0.00
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article

A dataset of entity and relation recognition of China&rsquo;s major grain varieties in 2023

Lin Hu, Xiaoyu CHEN, Yang Yang, Yu Zhang et al.
China Scientific Data
Remote Sensing and Land Use
article

A dataset of entity and relation recognition of China&rsquo;s major grain varieties in 2023

Lin Hu, Xiaoyu CHEN, Yang Yang, Yu Zhang, Yang Peng, Xiaoli WANG
article en

Abstract

<p indent="0mm">With the continuous growth of the world population and the development of science and technology, the combination of agriculture and advanced scientific technology is characterized by sustainability, digitization, and precision production. In this development process, the leverage of large-scale, high-dimensional, structured, semi-structured, and unstructured agricultural data generated over time has become a big challenge in guiding agricultural breeding and production. To address this challenge, we constructed a dataset of crop-centric entity and relation recognition for major grain crops, aiming to support breeding and production activities more effectively. Firstly, domain-specific literature was collected and processed for major grain crops such as rice, corn, and barley. Secondly, based on domain knowledge from the National Agricultural Science Data Center and plant ontology databases, we established a set of crop-specific entity relation classification standards. Finally, the dataset was supplemented with manually annotated data. The dataset includes entity recognition data for three main grain crops and relation recognition data, with a total of 33306 entity recognition records and 25846 relation recognition records, amounting to 4.58MB. The dataset has undergone professional data management and was reviewed and classified by agricultural experts through professional data management, ensuring its high quality and reliability. The dataset provides fundamental data for entity and relation recognition of main grain crops, which is beneficial for promoting the cultivation and production of main grain crops and holds significant practical application value.

China Scientific DataVol. 10(2)
Zero hunger
Openalex Percentile: Top 80%
Remote Sensing and Land Use
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