DOI: 10.57760/sciencedb.agriculture.00263
title: A dataset of entity and relation recognition of China’s major grain varieties in 2023
title: 2023年中国农业主粮品种实体关系识别数据集

description: With the continuous growth of the world’s 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. This dataset includes entity recognition data for three main grain crops and relation recognition data, with a total of 33,306 entity recognition records and 25,846 relation recognition records, amounting to 47.2 MB. 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. This 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.
description: With the continuous growth of the world’s 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. This dataset includes entity recognition data for three main grain crops and relation recognition data, with a total of 33,306 entity recognition records and 25,846 relation recognition records, amounting to 47.2 MB. 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. This 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.