Poultry disease detection system based on knowledge graph

Through a poultry disease detection system based on knowledge graphs, combined with autopsy images and text descriptions, and using the Clip model and Neo4j database, the problems of misjudgment and lag in poultry respiratory disease detection were solved, and fast and accurate disease diagnosis and treatment recommendations were achieved.

CN120674035APending Publication Date: 2025-09-19HEBEI AGRICULTURAL UNIV.
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Patent Information

Application Number
CN202510766390.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-10
Publication Date
2025-09-19

AI Technical Summary

Technical Problem

The detection of poultry respiratory diseases is plagued by problems such as insufficient reliance on manual experience, delayed detection, and weak multi-source data fusion capabilities, leading to high risks of misjudgment, missed diagnosis, and disease transmission.

Method used

A poultry disease detection system based on knowledge graph is adopted. It combines autopsy images and text descriptions, uses the Clip model to identify symptom characteristics, obtains disease names, treatment methods and environmental influencing factors through knowledge graph, and constructs a Neo4j graph database for efficient query.

Benefits of technology

It achieves the immediacy and accuracy of poultry disease diagnosis, shortens the detection cycle, improves detection efficiency and accuracy, and ensures data reliability and system usability.

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Abstract

The invention relates to a poultry disease detection system based on a knowledge graph. The poultry disease detection system mainly comprises a data acquisition module and a disease recognition module, wherein the data acquisition module is used for reading poultry dissection pictures and corresponding text descriptions; and the disease recognition module is used for recognizing symptom features by adopting a Clip model according to the poultry dissection pictures and the corresponding text description, and acquiring corresponding disease names, treatment methods and environmental influence factors based on the symptom features and a knowledge graph. According to the application, through cooperative operation of the image recognition model and the knowledge graph, rapid, accurate and comprehensive detection of the poultry respiratory diseases can be realized.
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