Anika TT, Noman ZA, Ashraf MN, Sultana N, Pervin M, Khan MAHN. Machine learning in bovine sub-clinical mastitis: One-stop veterinary diagnostic model. Vet Res Notes. 2024 Apr. http://doi.org/10.5455/vrn.2024.d40.
This study aims to develop a machine learning model to identify bovine subclinical mastitis (SCM) by analyzing milk biomarkers, specifically electric conductivity (EC) and total dissolved solids (TDS). Field data on these biomarkers are processed through a central network system and cross-referenced with a library database. The standardized cutoff values for SCM detection are 6.16 mS/cm for EC and 3100 mg/L for TDS, based on samples from 108 cows. The system, currently under development, is intended to alert veterinary authorities or dairy owners about SCM onset for timely management.
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