Constructing Crop Portraits Based on Graph Databases Is Essential to Agricultural Data Mining

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 c...

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Bibliographic Details
Published in:Information (Basel) 2021, Vol.12 (6), p.227
Main Authors: Shi, Yue-Xin, Zhang, Bo-Kai, Wang, Yong-Xiang, Luo, Han-Qian, Li, Xiang
Format: Article
Language:English
Subjects:
agricultural analysis
Agricultural production
Agriculture
Big Data
Crop diseases
crop portrait
Crops
Data mining
graph database
graph schema
Knowledge bases (artificial intelligence)
Knowledge management
Knowledge representation
Ontology
Pesticides
Pests
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Summary: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.
ISSN:2078-2489
2078-2489
DOI:10.3390/info12060227