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AI for sustainability in food supply chain and pricing

School of Computing and Information Science

About the Project

Research Group: Computing, Informatics and Applications Research Group.

Proposed supervisory team
Dr Lakshmi Babu Saheer (
Dr Cristina Luca (
Dr Javad Zarrin (


Artificial Intelligence, Machine Learning, Data Science and Applications.

Summary of the research project

Artificial Intelligence has wide spread impact in every aspect of human life. This impact could potentially range from current day challenges of Climate change (Climate change AI, 2019) to building a sustainable future with alternate sources of energy and sustainability in food supply. Challinor, (2014) and Ray,, (2012) emphasize the importance of research in the areas of food production to control food supply and pricing, to build a sustainable future. Recent pandemic situation and threat of scarcity in food supply has highlighted need for identifying local food sources to maintain a steady and affordable flow of food in local communities.
There have been various studies to look into data analysis for identifying food production shocks and food price in global economy (Jones & Phillips, 2016). Recently, block chain has been extensively used to model food supply chain (Casino,, 2014). This greatly helps in traceability and proof of regulatory compliance. It is of great importance to identify the local supply chain to keep the prices and availability in check along with building a sustainable future.

This research will be focussed on identifying the key factors in the food supply chain. The research could be initiated by understanding features influencing food pricing and shocks by looking at historic data (FaoStat, 2019). Based on these build sustainable models of food supply and pricing such that locally grown products get to the local markets. Understanding the key expenditures in the food supply chain supports in not only ensuring food supply, but also affordable food pricing and greater profit to local farmers. In turn this should lead to reduction in the energy and costs used in transport/storage and contribute to climate change.

The project would involve extensive data collection for local produce and demands in a selected region (probably Cambridgeshire) to support this endeavour. The project will also look into detailed data analysis and machine learning models to build optimum solutions including block chain supply model to get the most efficient system in place. Some of this research may be carried out in collaboration with the global sustainability institute at Anglia Ruskin. In effect, this research is expected to build a tool to co-ordinate and build an efficient local food supply chain for a sustainable future.

Where you’ll study


Next steps

If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD ( In the section of the application form entitled ’Outline research proposal’, please quote the above title and include a research proposal.

Funding Notes

This project is self-funded.

Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website (View Website) as they become available.


Aled Jones and Alexander Phillip, 2016. Historic Food Production Shocks: Quantifying the Extremes, Sustainability open access journal, 8, pp.427.
Climate Change AI, 2019. Climate Change AI, [Accessed 25 November 2019]
Challinor, A.; Watson, J.; Lobell, D.B.; Howden, S.M.; Smith, D.R.; Chhetri, N., 2014. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Chang., 4, pp.287–291.
Ray, D.K., Ramankutty, N., Mueller, N.D., West, P.C., Foley, J.A., 2012. Recent patterns of crop yield growth and stagnation. Nat. Commun., 3, pp.1293.
Fran Casino, Venetis Kanakaris, Thomas K. Dasaklis, Socrates Moschuris, Nikolaos P. Rachaniotis, 2019. Modeling food supply chain traceability based on blockchain technology, IFAC-Papersonline, 32(13), pp.2728-2733.
Food and Agriculture Organisation of the United Nations Statistical Database. Available online ( (accessed on March 2020).

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