Suresh Neethirajan
Resilience, the capacity of an animal to recover rapidly and completely after a perturbation, has long been recognised but seldom quantified with precision. What has changed is the computational machinery now capable of measuring it. Climate variability, emerging disease pressures and increasingly complex production environments require systems that prioritise adaptive capacity rather than simple output stability. Here we show that resilience can be operationalised through the integration of multimodal sensing, state space modelling and machine learning. Continuous data streams from accelerometers, thermal and RGB imaging, milk meters, rumen boluses and behavioural trackers capture fine scale signatures of disturbance and recovery. Advanced filtering architectures reconstruct latent physiological states, while neural, hybrid and mechanistic statistical models extract recovery dynamics with accuracies between 80 and 99 percent and provide early warning of health compromise several days in advance. Across species, indicators based on variance, autocorrelation and area under the curve reliably distinguish resilient from fragile phenotypes, with heritabilities ranging from 0.026 to 0.432, allowing incorporation into genomic selection. Composite indices that combine production, behaviour, physiology and environmental context provide interpretable and challenge specific scoring frameworks for breeding and real time management. Despite these advances, governance mechanisms for responsible AI deployment remain incomplete, increasing the risk of misclassification, opacity and productivity centred optimisation. Resilience phenotyping is therefore both a technical and ethical opportunity. When paired with transparent data governance and welfare focused design, digital systems can shift livestock management from reactive oversight to anticipatory care. Together, sensor enabled phenotyping and computational modelling provide a scalable and welfare centred route for advancing adaptive livestock systems.