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Projects

Below is the list of some projects I am currently leading and working along side my collaborators.

 I am also involved with colleagues  as Co-Investigators in other projects on cattle and sheep health and welfare. 

 Research Projects

Automatic detection of activity and lameness

Funded by Innovate UK and BBSRC we are working alongside our Industry partner Intel to develop a novel system and algorithms for automatic detection of lameness in sheep and activity. Recently we got funding to explore this to develop system for monitoring activity in calves.  Project "EL4L" (2016-2017), BBSRC TFP (2018-2019) 

Project value : £242K; £20K

Lameness management and monitoring App

We are academic lead on project funded by Innovate UK , where we are working with Industry partners Dunbia and Farm Wizard to develop a novel hardware and software system for lameness data collection and feedback. We are trialing this technology on farms, the lameness app is available on google and itunes store. Project "SPILAMM" (2015-2019), Project value: £869K

Young-stock health and welfare

The Y-Ware project is developing an IoT (Internet of Things) based data collection solution, with sensors specifically developed for young-stock (14 weeks) and a Decision Dashboard backed up by comprehensive data analytics, driven by Vet science-informed algorithms, allowing Farmers, Vets, Retailers (and ultimately the consumer), with accurate Animal Health and Welfare assessments to reduce and eliminate the cause of the current losses.

 

This project addresses the Innovate UK priority area ‘increase yield, quality and sustainability in agriculture and food production’ by focusing on advanced and precision engineering,  fighting antimicrobial resistance and individualised nutrition and healthcare for dairy and beef young-stock.  We are leading  data analytics on this project working alongside our industry partner BT and technology partner Prognostix. (2017-2020) Project value: £1.13M

Novel data fusion and machine learning approaches to Internet of things livestock data 

An effective, automated precision monitoring solution would be of huge benefit for the early detection of disease in cattle, however, there are no algorithms for cattle health yet that have high predictive value for early disease detection.  

We have teamed up with Industry partners Prognostix Ltd and British telecommunications and by using ‘big data’ gathered from multiple sensors on cow health, production and activity this BBSRC PhD (2018-2022) aims to explore following key questions:  
1. What methods are best for data fusion (signal level fusion, feature level fusion or decision level fusion for predicting cattle health (disease events: (respiratory disease/diarrhoea/lameness/high somatic cell counts) and production (milk, weight gain) and what are penalties of those (with respect to performance and implementation?  
2. What features are important and have higher predictive value for early prediction of disease? How early can we predict a disease event on cattle farm using this metadata? 

Antimicrobial usage on sheep and beef farms

 

There’s a growing pressure to reduce antimicrobial (AM) use in animal health. However, to effectively promote this change in treatment, the industry needs sound benchmarking data on usage patterns and a good understanding of the practices and attitudes that exist around antimicrobials on farms and among veterinary clinicians.

This AHDB funded project is using multiple data sources to quantify antimicrobial use on UK sheep and beef farms. It also aims to improve understanding of farmer and vet decision making, with the overall objective of supporting the responsible, reduced use of antimicrobials. (2017-2020) Project value: £70K

Evidence based farm decisions for lamb production

Most producers aim to continually improve their farming systems. However their decisions are often made on the basis of single, easily available parameters, such as scanning percentage (proportion of sheep pregnant after breeding), with no real reference to overall flock performance. We’re working to enable producers to make more effective decisions by creating an evidence-based, holistic model of factors they can control which effectively influence flock performance. We’re doing this by using two rich, complementary agri-informatics data sets. 

 

On farms, flock performance is about a combination of factors – both at flock and individual animal level – encompassing a whole range of decisions on management and husbandry. Most of these factors are connected and these connections need to be accounted for in decision making. (2016-2019) Project value: £70K

Get in touch if you will like to discuss potential projects or  like to find out more about our current research

Funders and Industry Partners

 
 
 

Consumer perception of Dairy

The aim of our multidisciplinary research is to provide deeper insights into the attitudes of a variety of stakeholders, including dairy consumers and the general public.

 

We’re exploring their perspectives on the management and wellbeing of dairy cows under different farming environments, with the aim of better informing, influencing and supporting dairy farmers. This project is funded by AHDB. (2017-2020) Project value: £70K