Broiler abnormity monitoring method and system based on data analysis, medium and terminal

By installing multi-angle cameras in broiler feeding sites, collecting image data, data analysis and feature extraction, the problem of time-consuming and poor accuracy of manual verification of existing broiler abnormality monitoring methods is solved, and efficient and accurate broiler abnormality monitoring is achieved.

CN120032393AActive Publication Date: 2025-05-23LANGFANG ACAD OF AGRI & FORESTRY SCI
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Patent Information

Application Number
CN202510306154.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-14
Publication Date
2025-05-23
Estimated Expiration
2045-03-14

AI Technical Summary

Technical Problem

The existing broiler abnormality monitoring methods rely on manual verification of video streams, resulting in a large amount of manpower and material resources, time-consuming and poor accuracy and effectiveness.

Method used

The chicken image data is collected by cameras located at different monitoring angles, the position area identification is analyzed for pixel splitting, the feature image is extracted, and the feature image is compared according to the comparison time length to determine the abnormal area. If the exception area matches the preset contour range, an exception monitoring result is generated.

Benefits of technology

Real-time monitoring of broilers is achieved, reducing manpower and material consumption, reducing work time for staff, and greatly improving effective monitoring of broilers' small situations, thereby improving the accuracy of chicken abnormal monitoring.

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Abstract

The invention discloses a broiler abnormity monitoring method and system based on data analysis, a medium and a terminal, relates to the technical field of data processing, and mainly aims to solve the problems of accuracy and effectiveness of existing broiler abnormity monitoring. Comprising the following steps: acquiring data of multiple broiler images in a broiler feeding place through cameras located at different monitoring angles; analyzing a position area identifier in the broiler image data, and performing pixel splitting on the broiler image data according to the position area identifier to obtain a plurality of block-level image data; performing feature extraction on the plurality of block-level image data to obtain feature images, and comparing the plurality of feature images according to a comparison time length to determine an abnormal region; and if the abnormal area is matched with a preset broiler abnormal contour range, generating a broiler abnormal monitoring result.
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