Individual laying rate identification system for laying hen high-density breeding based on AI

By adopting an AI-based individual egg laying rate recognition system in laying hen farms, the problem of low-yield chickens is accurately identified and managed, and the problem of low-yield chickens is solved, which has achieved a significant improvement in egg laying rate and an increase in economic benefits.

CN119721944AInactive Publication Date: 2025-03-28NANNING AQUATIC ANIMAL HUSBANDRY & VETERINARY TECH PROMOTION STATION
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Patent Information

Application Number
CN202411560587.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-04
Publication Date
2025-03-28
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

When identifying and increasing the egg production of laying hens, the prior art has the problem of low accuracy through manual methods, and it is impossible to accurately optimize and manage low-yield chickens.

Method used

The individual egg laying rate recognition system for high-density breeding of laying hens is adopted based on AI. The collection module, analysis module and low-yield chicken identification module are used to accurately identify low-yield chicken flocks. The identification system includes binocular cameras, processing units, low-yield chicken identification modules, etc., and low-yield chicken identification modules are used to identify low-yield chickens.

Benefits of technology

The egg laying rate of the farm has been significantly improved, the low-yield chickens have been accurately identified, and the overall egg laying rate has been improved through targeted management, bringing about an improvement in economic benefits.

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Abstract

The invention discloses an individual laying rate recognition system for high-density breeding of laying hens based on AI in the technical field of laying hen breeding, and the system comprises a collection module which is used for collecting the number data of each hencoop in a farm, the egg production in each hencoop, and the appearance characteristics of each chicken in each hencoop; the analysis module is used for receiving an egg yield threshold value, set by a user, of a single coop, and marking the coop with the egg yield lower than the egg yield threshold value as a coop to be detected; the low-yield chicken recognition module is used for receiving set influence factors influencing the egg yield of the chickens, extracting real-time appearance characteristics of each chicken in the to-be-detected chicken coop, extracting the influence factors influencing the egg yield of the chickens from the appearance characteristics of each chicken in the to-be-detected chicken coop, and sending the extracted influence factors to the low-yield chicken recognition module; and outputting a low-yield chicken recognition result based on the number and degree of the extracted influence factors influencing the egg yield of the chickens. According to the invention, the low-yield chicken flocks are accurately identified through the acquisition module, the analysis module and the low-yield chicken identification module, so that traditional manual identification of the low-yield chicken flocks is replaced.
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