Improving cow health and milk quality with artificial intelligence

Targeted research priorities

Health and welfare of dairy cattle
Rapid, accurate detection of the microorganisms responsible for mastitis would speed up treatment and reduce the use of antibiotics to treat this disease in dairy cows.

Indicators and control tools
The artificial intelligence tool developed could enhance dairy herd health management by effectively detecting mastitis-causing pathogens.

Milk quality
Rapid detection of pathogenic or spoilage microorganisms could make it possible to adapt milk treatments, thereby improving its quality and optimizing its use for various dairy products.

Project summary

Mastitis, infections of the mammary glands in dairy cows, represents a major challenge for the dairy industry due to its negative impact on milk production and quality. Rapid detection of the pathogens responsible is essential for effective disease management.

This research project aims to develop an artificial intelligence (AI) tool capable of rapidly and accurately identifying mastitis pathogens and spoilage microorganisms in milk. This tool will use advanced algorithms to analyze microbiological data and provide faster diagnoses. This technology has proved its worth in the medical field (detection of bacteria in urine) and the team believes it has potential for the dairy sector.

The AI tool could enable faster, more accurate detection of mastitis-causing microorganisms, thereby reducing economic losses and improving milk quality. Producers will be able to take corrective action more quickly, contributing to better herd health management and more sustainable milk production.

Expected results

The aim of this research is to develop an artificial intelligence tool combined with mass spectrometry to rapidly identify the microorganisms responsible for bovine mastitis. Identifying bacterial and fungal species within 1-4 hours, with high specificity and sensitivity, would limit the use of broad-spectrum antibiotics, favouring more specific treatments and reducing the potential for antimicrobial resistance.

Early detection of mastitis and dairy contaminants would improve milk quality. Processors could adapt milk treatments and optimize its use for various dairy products.

This technology would offer significant economic benefits to the dairy sector by improving animal health and reducing the economic damage associated with product spoilage and recall. At the end of the project, the AI tool could be scaled up for use in analytical laboratories and large dairy companies.

Main achievements

• Training a master’s student and a research professional
• Presentation of results at the Forum Techno and Novalait annual meeting
• Lectures and poster presentations at national and international conferences
• Writing scientific articles
• Development potential for patented technology