Intelligent monitoring method and system for rumination behaviors of cattle herds

By intelligently processing cattle herd monitoring video data, identifying and filtering images of the cattle's mouth area, generating optical flow field sequences, and using a classifier to determine rumination behavior, the problem of low accuracy in cattle herd monitoring has been solved, improving monitoring precision and efficiency.

CN121482830AActive Publication Date: 2026-02-06JINKAI TECH (DALIAN) CO LTD
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Patent Information

Application Number
CN202511890241.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-15
Publication Date
2026-02-06
Estimated Expiration
2045-12-15

AI Technical Summary

Technical Problem

Current technologies for monitoring bovine rumination behavior have low accuracy and are severely affected by bovine activity, making precise monitoring difficult.

Method used

By acquiring cattle herd monitoring video data, identifying images of the cattle's mouth area, performing occlusion detection, and filtering target cattle mouth area images according to the occlusion ratio, an optical flow algorithm is used to generate an optical flow field sequence, which is then combined with a preset classifier to determine rumination behavior.

Benefits of technology

It significantly improves the accuracy and efficiency of monitoring individual rumination behavior in cattle herds, is suitable for large-scale farming scenarios, reduces the impact of invalid data, and achieves automated judgment of rumination behavior.

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Abstract

The invention relates to the technical field of intelligent livestock management, in particular to an intelligent monitoring method and system for rumination behaviors of cattle individuals, and solves the technical problem of low monitoring accuracy in the prior art. The method comprises the following steps: acquiring cattle herd monitoring video data, and performing cattle mouth part identification on image frames in the cattle herd monitoring video data through a target identification model to obtain a cattle mouth area image in each image frame; performing shielding detection on the cattle mouth area image in each image frame, and screening out a target cattle mouth area image from the cattle mouth area images in each image frame according to an obtained shielding proportion; processing the screened target cattle mouth area image through an optical flow algorithm, and generating an optical flow field sequence representing cattle mouth movement; and inputting the optical flow field sequence into a preset classifier, judging whether the cattle is in a rumination behavior, and outputting a monitoring result of the rumination behavior.
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