ArticleScientific reports2020
Automated prediction of mastitis infection patterns in dairy herds using machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Machine learning-based prediction of subclinical mastitis in large-scale dairy herds using a locally established somatic cell count threshold.Scientific reports · 2026Article
- - Invited Review - Biosensors in precision livestock farming in dairy production: decoding animals' needs.Animal bioscience · 2026Article
- Milk Biomarkers and Herd Welfare Status in Dairy Cattle: A Machine Learning Approach.Veterinary sciences · 2025Article
- The Use of Selected Machine Learning Methods in Dairy Cattle Farming: A Review.Animals : an open access journal from MDPI · 2025Review
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- AI and Data Analytics in the Dairy Farms: A Scoping Review.Animals : an open access journal from MDPI · 2025Review
- Machine learning-based clinical mastitis detection in dairy cows using milk electrical conductivity and somatic cell count.Frontiers in veterinary science · 2025Article
- Current Trends in Artificial Intelligence and Bovine Mastitis Research: A Bibliometric Review Approach.Animals : an open access journal from MDPI · 2024Article
- A New Method to Detect Buffalo Mastitis Using Udder Ultrasonography Based on Deep Learning Network.Animals : an open access journal from MDPI · 2024Article
- The Prediction of Clinical Mastitis in Dairy Cows Based on Milk Yield, Rumination Time, and Milk Electrical Conductivity Using Machine Learning Algorithms.Animals : an open access journal from MDPI · 2024Article
- Human-computer interactions with farm animals-enhancing welfare through precision livestock farming and artificial intelligence.Frontiers in veterinary science · 2024Article
- Basic concepts, recent advances, and future perspectives in the diagnosis of bovine mastitis.Journal of veterinary science · 2024Review
- Factors Associated with Antimicrobial Stewardship Practices on California Dairies: One Year Post Senate Bill 27.Antibiotics (Basel, Switzerland) · 2022Article
- The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer.Frontiers in oncology · 2022Article
- Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study.Sensors (Basel, Switzerland) · 2021Review
- Milk microbiome in dairy cattle and the challenges of low microbial biomass and exogenous contamination.Animal microbiome · 2021Article
- Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows.Scientific reports · 2021Article
- Rule Discovery in Milk Content towards Mastitis Diagnosis: Dealing with Farm Heterogeneity over Multiple Years through Classification Based on Associations.Animals : an open access journal from MDPI · 2021Article
- Machine Learning in Agriculture: A Comprehensive Updated Review.Sensors (Basel, Switzerland) · 2021Review
- Machine-learning based prediction of Cushing's syndrome in dogs attending UK primary-care veterinary practice.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mastitis in dairy cattle is extremely costly both in economic and welfare terms and is one of the most significant drivers of antimicrobial usage in dairy cattle. A critical step in the prevention of mastitis is the diagnosis of the predominant route of transmission of pathogens into either contagious (CONT) or environmental (ENV), with environmental being further subdivided as transmission during either the nonlactating "dry" period (EDP) or lactating period (EL). Using data from 1000 farms, random forest algorithms were able to replicate the complex herd level diagnoses made by specialist veterinary clinicians with a high degree of accuracy. An accuracy of 98%, positive predictive value (PPV) of 86% and negative predictive value (NPV) of 99% was achieved for the diagnosis of CONT vs ENV (with CONT as a "positive" diagnosis), and an accuracy of 78%, PPV of 76% and NPV of 81% for the diagnosis of EDP vs EL (with EDP as a "positive" diagnosis). An accurate, automated mastitis diagnosis tool has great potential to aid non-specialist veterinary clinicians to make a rapid herd level diagnosis and promptly implement appropriate control measures for an extremely damaging disease in terms of animal health, productivity, welfare and antimicrobial use.
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Registered trials
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