Active Psychology & Behaviour Food & Agriculture

The importance of sleep: using AI (video-based motion capture systems) to improve the health, resilience and productivity of dairy cows

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Dairy cows are losing sleep, and it is costing both their health and farm productivity. Sleep is one of the most overlooked factors in farm animal welfare. Chronically disrupted sleep alters brain function, changes physiology, and reduces a cow’s resilience to stress. Yet farmers have no practical way to monitor whether their animals are sleeping well. This project aims to fill that gap by developing an automated, video-based system that can identify when a cow is awake, asleep, or eating—without needing to attach sensors to the animal. The team will first collect video footage of ten cows in a commercial indoor dairy system, alongside EEG and actigraphy data to confirm sleep states. They will then train artificial intelligence to recognise those states from video alone. Finally, they will validate the system by making small changes to light and bedding to see if the AI correctly detects sleep disruption. If successful, this system could give farmers a simple, non-invasive tool to monitor sleep as a routine indicator of health and welfare. Better sleep means more resilient cows, improved productivity, and a stronger ethical case for how dairy animals are kept.

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This project focuses on sleep in dairy cows. Sleep is one of most critical and often overlooked factors affecting farm animal health and production. It is fundamental to animal well-being and chronically disrupted sleep leads to a number of issues such changes in brain function, changes in physiology and reduced resilience to stress. Optimal sleep is, therefore, critical to the well-being of animals and, in the context of farm animals, has the potential to greatly impact the animal's level of sustainability and production. Understanding and being able to improve farm animal sleep is, thus, both an ethical and economic priority. There are three main stages of the project which are: 1. To collect video footage of wake and sleep states of 10 cows within a commercial indoor dairy system alongside EEG and actigraphy data; 2. To use artificial intelligence techniques to train computer vision technology to identify different sleep, wake and eating states from the videos footage by cross-referencing the behavioural, EEG and actigraphy data; 3. To validate the automated sleep video analysis system by running a sleep disturbance trial through small changes in the animal's husbandry (light and bedding).

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