2021/01/06 by Cathal Ryan, Ryan, Cathal, Christophe Guéret +9
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Immunology and Microbiology · #FOS: Computer and information sciences #Genetic and phenotypic traits in livestock #Machine Learning (cs.LG) #Microbial infections and disease research #Milk Quality and Mastitis in Dairy Cows
paper · pdf · doi:10.48550/arxiv.2101.02188
openalex publication_date 2021/01/06 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Mastitis is a billion dollar health problem for the modern dairy industry, with implications for antibiotic resistance. The use of AI techniques to identify the early onset of this disease, thus has significant implications for the sustainability of this agricultural sector. Current approaches to treating mastitis involve antibiotics and this practice is coming under ever increasing scrutiny. Using machine learning models to identify cows at risk of developing mastitis and applying targeted treatment regimes to only those animals promotes a more sustainable approach. Incorrect predictions from such models, however, can lead to monetary losses, unnecessary use of antibiotics, and even the premature death of animals, so it is important to generate compelling explanations for predictions to build trust with users and to better support their decision making. In this paper we demonstrate a system developed to predict mastitis infections in cows and provide explanations of these predictions using counterfactuals. We demonstrate the system and describe the engagement with farmers undertaken to build it.