Revolutionizing estrus detection and herd health surveillance with ai and thermal imaging in dairy animals

Efficient reproductive and health management is essential for sustainable dairy production, yet timely detection of estrus and early recognition of disease remain challenging under routine farm conditions. In dairy animals especially buffaloes, weak or poorly expressed estrus behaviour can result in missed opportunities for artificial insemination, delayed conception and prolonged calving intervals. The subtle physiological changes associated with disease may remain undetected until recognizable clinical signs develop. Conventional visual observation is also influenced by herd size, observation frequency, environmental conditions and the experience of farm personnel. Advances in precision livestock farming provide opportunities to complement conventional observation with continuous, non-invasive monitoring. Infrared thermography can capture surface temperature patterns, while artificial intelligence and computer vision can analyse animal identity, movement and behaviour. Integrating these information sources may help identify patterns associated with estrus, including poorly expressed estrous events, while also flagging abnormal thermal or behavioural changes that could indicate an underlying health problem. Such a system could serve as an early-warning tool for both reproductive and herd health management. Real-time digital alerts could assist farmers and veterinarians in prioritizing animals for reproductive intervention or clinical examination. Environmental influences, individual variation, image quality, algorithmic reliability and field validation remain important considerations. The integration of thermal imaging, behavioural monitoring with artificial intelligence represents a promising approach towards objective, non-invasive and data-assisted reproductive and health surveillance in dairy herds.