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PROoFD IT!: Provenance of Food Delivery through IoT

By Naomi Jacobs
The PROOFD-IT project is about using technology to help track the food delivery process and monitor important information (such as temperature) at every stage. We want to design new systems which make use of the Internet of Things to do this, but we want to make sure we are meeting actual existing needs rather than jumping in and making assumptions about what might help. For that reason, the first step for us is to understand how food delivery works right now, and what currently happens between the production of food and its delivery to the customer. With that in mind, the first thing we set out to do was visit our three business partners and shadow the entire ordering and delivery process to understand more about it.
Spending time with our partners meant seeing ‘behind the scenes’ of food production, including aspects that often might be taken for granted. We got to see giant freezers, followed the route that ingredients take through kitchen preparation to become finished products, and joined delivery drivers on their routes around Aberdeen. In this way, we can build up a complete picture and see where there are opportunities for technology to help; for example certain points in the current system where temperature readings are taken and written down on paper for record-keeping.
Having an interdisciplinary team working on the project means that we can use these observational research methods to gain insight and understanding into how things work in the real world, before using technical skills to design new solutions. By working together in this way we can create and test new systems that bring new opportunities and will improve food safety and build customer trust. Once we’ve finalised our user journeys, and used them to design our new systems, the next step will be to test them out in the real world with our partners!

Pilot project: use of sensors to improve pig productivity

By Rachel Norman and Jason Adair

We were delighted to receive funding from the Internet of Food things Network which allows us to apply statistical and machine learning methods to sensor datfrom a pig farm. We are based in the Computing Science and Mathematics Department (CS&M) at the University of Stirling

(https://www.stir.ac.uk/about/faculties/natural-sciences/our-research/computing-science-and-mathematics-research/) . Within Stirling we have Rachel Norman (@AFSRachel https://www.stir.ac.uk/people/255946) who is Professor of Food Security and Sustainability, Richard Connor (https://www.stir.ac.uk/people/939088) who is Professor of Computing Science and our postdoc Jason Adair (https://www.stir.ac.uk/people/256928) . We are working with collaborators at the Agricultural Engineering Precision Innovation Centre (Agri-EPI Centre) in Edinburgh (https://www.agri-epicentre.com/) and the Scottish Pig Producers association (http://www.scottishpigs.coop/).

Whilst much of the research in CS&M is interdisciplinary in nature, this is the first time we have worked with the pig sector, and so this project gives us an excellent opportunity to learn more about this production system. We started out with a meeting at the Agri-Epi centre in which we discussed all of the types and sources of data available at one of their satellite pig farms. This ranges from high tech data from state-of-the-art 3D cameras which estimate pig weight, to handwritten bits of paper which record the average batch weights at two points in their growth cycle. It also includes environmental data from sensors in the sheds which record temperature, humidity and ammonia levels; data on what and how much they eat each day; abattoir data on the weight and quality of each of the 180 carcasses produced each week, and data from the farm’s very own weather station. This meeting was followed quickly by a _very_ early morning run up to the farm to see what the data looked like in the flesh.

Rachel and pig
Rachel meeting one of the farm’s youngest residents

It was great to have the opportunity to ask lots of questions about how data is currently used on the farm, and what we could do to help. So, although it is early days, we are looking forward to collating the data from different sources and trying to decide whether the sensors could be used as a management as well as a monitoring tool. It’s early days but it certainly won’t be boaring!