System and method for real-time detection of mastitis indicators in bovine milk

A system using sensors and advanced models for real-time mastitis detection in milk addresses the challenge of predicting subclinical and preclinical mastitis, enabling timely interventions and improving milk quality.

WO2026083325A1 Publication Date: 2026-04-23MILKSENSE LTD
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Patent Information

Application Number
PCT/IB2025/060565
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2025-02-09
Filing Date
2025-10-16
Publication Date
2026-04-23

AI Technical Summary

Technical Problem

Existing milk quality detection systems fail to accurately predict subclinical and preclinical mastitis and do not provide real-time predictions of impending mastitis onset, lacking the capability to determine the probability of clinical conditions within specific time intervals.

Method used

A system that employs sensors to measure various mammalian milk characteristics, including fat, protein, lactose, and somatic cell count, using machine learning and rule-based models to analyze sensor data in real-time, providing predictions and feedback for preventative measures.

Benefits of technology

Enables real-time detection and prediction of mastitis, allowing for timely intervention and improving milk quality by identifying impending clinical, subclinical, and preclinical conditions, thereby enhancing herd health management and reducing antibiotic use.

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Abstract

Milk quality can be compromised by physiological conditions affecting dairy mammals, notably mastitis. Mastitis is an inflammatory disease of the mammary gland that may pose significant health and economic challenges. To address this, a system is proposed for monitoring at least one mammal to predict the onset of a clinical condition, such as, for example, mastitis. The system may be configured to sense one or more characteristics descriptive of mammal-related information, receive sensor data corresponding to those characteristics, and process the data to forecast the onset of mastitis within an upcoming time interval. Early detection enables timely intervention, promoting animal welfare and preserving milk quality.
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