Mastitis, particularly its subclinical form, remains a major concern in dairy farming, causing substantial economic losses through reduced milk yield, poor milk quality, and increased treatment costs. In India, where dairy farming sustains rural livelihoods, the hidden burden of the disease is particularly significant. Beyond its economic impact, subclinical mastitis poses public health risks by facilitating the spread of pathogens and contributing to antimicrobial resistance. Early and accurate detection of mastitis is essential for effective control. Traditional diagnostic tests often lack the sensitivity, specificity, or pathogen-level resolution required for timely intervention. In contrast, recent advances in machine learning, nanotechnology, biosensors, and molecular diagnostics enable rapid and targeted detection, supporting timely treatment, minimizing antibiotic use, and strengthening the economic and operational resilience of the dairy sector.
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