Abstract
Neo4j is a graph database that can use not only data, but also data relationships. Crop portraits, a kind of property graph, model the crop entity in the real world based on data to realize the networked management of crop knowledge. The existing crop knowledge base has shortcomings such as single crop variety, incomplete description, and lack of agricultural knowledge. Constructing crop portraits can provide a comprehensive description of crops and make up for these shortcomings. This research used agricultural question-and-answer data and popular science data obtained by text crawling as the original data, selected labels to establish a crop portrait that including three categories (crops, pesticides, and diseases and pests), and used the graph database (Neo4j) to store and display these portrait data. Information mining found that the crop portrait revealed the occurrence trend of diseases and pests, exhibited a nonintrinsic connection between different diseases and pests, and provided a variety of pesticides to choose from for control of diseases and pests. The results showed that constructing crop portraits is beneficial to agricultural analysis, and has practical application values and theoretical research prospects in the field of big data analytics.
Highlights
Crop production is the basis of agricultural production, and agricultural production is the foundation for human survival
We found that crop entities were more important than pesticide entities and disease and pest entities, and fruit entities were more important than nonfruit entities
The results showed that the construction of crop portraits based on graph theory is very necessary and useful for agricultural data mining, which is helpful for agricultural analysis and guiding agricultural production
Summary
Crop production is the basis of agricultural production, and agricultural production is the foundation for human survival. Agriculture is an obvious and important target of big data, and data-mining technology provides accurate crop-yield estimates for agricultural production [2]. Neither the knowledge graph nor the user portrait can fully describe the crops in agricultural analysis. Constructing crop portraits based on graph databases can realize crop-centric labeling and description of crops; can be applied to the efficient extraction, management, and sharing of agricultural knowledge; and achieve in-depth and accurate analysis of agricultural affairs. Based on the graph theory, this research used Neo4j as the storage method and display form to construct crop portraits. The results showed that the crop portrait based on graph databases provides good guidance on agricultural production, and solves problems that are difficult to directly display with general science data.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
