​Configuring our food systems to meet global nutritional needs will require a much broader focus than farming and agriculture. With the challenge of climate change and pressure on environmental resources, innovation is needed at a systems-level, all the way from field to fork. As the SFN’s 2023 Annual Conference showed, projects funded by the network are targeting every intervention point to deliver ‘Smart, Sustainable and Safe Food Systems.’

Enabling Net Zero Food & Drink – the need for End to End innovation by Professor Ian Noble, VP R&D – Research & Analytical, Mondelez International
The global scale and multifaceted nature of food systems makes feeding the human race a “mind-bendingly complex” problem that can’t be fixed using simple, single-point interventions. But this creates opportunities to introduce transformational change that ripples outwards across different levels. Achieving this, however, requires systems-thinking that goes beyond seeing food chains as linear processes. Instead, we need to adopt ‘design-led’ approaches that explore problems with creativity, whilst keeping stakeholder needs front and centre. Trying to introduce even an apparently simple change – such as reducing packaging on chocolate Easter eggs – will only be successful if producers still have a sustainable business and customers accept it.
This approach might sound daunting, but it also creates a space for conversations that throw the door open to new possibilities of doing things – and for collaboration across different disciplines. As Ian said, “If we approach the problems through a design mindset, this opens up new areas of creativity. Furthermore, food is such a broad area that there is space for everyone. Whatever your discipline – whether it is artificial intelligence or crop science – there is room for you to engage. And with the scale of the transformation that has to happen across the board, we will need everyone to be part of it.”

The featured projects:

Urban Cultivate: Integrating environmental, spatial, and social data to assign food supply chain functions across urban space by Dr Daniel Evans, Cranfield University
Urban Cultivate is exploring whether under-utilised vacant spaces within cities can be converted into growing centres for self-sufficient, local food systems. In a pilot study based in Islington, London, they used satellite images to identify 100 underused sites around Tufnell Park Road, then collected data on a cluster of these – including air and soil quality, microclimate, urban connectivity and connection to utilities. Following this, the team applied STFC Data Science Capabilities to design an optimization model which could calculate each site’s suitability value for different food-growing functions – from cultivation and composting right through to food exchange – which is then presented in an accessible, public dashboard.

Increasing the Nutrient-Use Efficiency and Crop Productivity of Hydroponics using SMART Sensors and 3D-Multispectral Crop Imaging Project PI: Professor Chungui Lu, Nottingham Trent University. Presented by Dr Gadelhag Mohmed, Nottingham Trent University
Could artificial intelligence (AI) and machine learning reveal ‘ideal plant growth recipes’ hidden in large datasets, and help make vertical farming systems more resource efficient? To investigate, this project generated Big Data from indoor growing systems by measuring environmental variables using a range of sensors, and combining this with output data such as shoot weight and leaf area. Using the STFC Hartree Centre, they constructed a cloud computing and data processing platform to develop a neural network AI model which could predict optimum growing recipes. These recipes are now being validated, and the team hope to refine the model with tailored algorithms for specific crops.
Find out more in this SFN+ blog post

Circular urban vertical farming. Data, models and optimisation of waste flows by Professor Peter Ball, University of York
Municipal waste could be a significant resource for circular, sustainable urban agriculture but without the proper data, these flows can’t be captured. Working with a vertical farm in York, this project developed two digital demonstrators to model how waste flows from urban sources could be optimised to provide a) feed nutrients for crops and b) construction materials. With support from IntelliDigest (see below), the team analysed the simulated data to see how closely the output matched the nutrient requirements of growing crops. The team also worked with a brewery and WASWARE to create biocomposite materials using grain waste from York beer production to demonstrate the proof of concept.
Find out more in this SFN+ blog post

Building smart urban farming data systems: A case study by Professor Catur Sugiyanto, Universitas Gadjah Mada, Indonesia
By combining digital technologies and geographic information systems with farmer surveys, this project took a deep dive into exploring whether urban farming can improve food security for vulnerable urban dwellers in Southern Asia, focusing on Yogyakarta in Indonesia. Their findings revealed that Yogyakarta has a diverse urban farming system, with some areas of the city specializing in specific products – such as fish in the south west regions. The surveyed families farmed not only for self-consumption but also to sell produce for additional income. Notably, a significant proportion of urban farm managers are women, enabling them to become more active in the economy.
“We found evidence that urban farms really can increase the incomes of families in Southern Asia. For our case study of Yogyakarta, household incomes of urban farmers were 50% over the regional minimum wage.” Professor Catur Sugiyanto, Universitas Gadjah Mada,

Spotting the Rust: Tracking disease progression by static and aerial imagery by Dr Melina Zempila, Science and Technology Facilities Council
This proof-of-concept project aims to develop a hyperspectral imaging-based classification system to enable early detection of a devastating wheat disease, yellow rust fungus. To inform the model, the project team combined hyperspectral and multispectral imaging data from laboratory-grown plants at different stages of rust development with aerial images of healthy and diseased wheat crops. Both approaches revealed that the reflectivity of wheat plants over certain wavelengths markedly increases during infection with yellow rust. From this, the team developed classification algorithms and differential approaches to identify diseased plants from novel data.

Novel process for extracting nutrients in food waste for sustainable and resilient urban farming and food packaging by Dr Ifeyinwa Rita Kanu, IntelliDigest
IntelliDigest has ambitious plans to catalyse the adoption of circular food systems, particularly through recovering bionutrients from waste streams using bioreactors containing natural digestive enzymes. But this will require real-time information on food waste composition, to optimise the combination of enzymes used. With STFC’s Central Laser Facility, IntelliDigest have explored a range of spectroscopic techniques (including near- and mid-infrared, and Raman Spectroscopy) that may be suitable for developing into a photonic sensor. Using the STFC Dafni platform, they have also developed a pilot interface which tracks the economic and environmental impact of diverting different food waste streams.
Find out more in the SFN+ blog post.
“Pilot growing studies indicate that our biodigest product has high macro- and micro-nutrient content for food production – and all of this is based on harnessing the power of natural enzymatic processes.” Dr Ifeyinwa Rita Kanu, IntelliDigest

SFN+ Strategic Partnerships Project: Digital Twin for FSA inspection process by Professor Sonal Choudhury, University of York
Following an SFN+ sandpit event, this consortium was launched to explore how advanced technology interventions – particularly digital twins – could help upgrade Food Standards Agency (FSA) meat processing and inspection operations from the 18th to the 21st century. Using discrete-event simulation (DES) modelling (which simulates processes where events occur at specific instances in time), the team produced a model of a pig abattoir process flow, enabling different scenarios to be tested. In particular, the model enables virtual testing of technology, people, and process configurations to explore ‘what-if’ scenarios to predict and optimise the system behaviour.

​You can watch the recorded session here.

Dr Jens Jensen describes himself as ‘a mathematician, scientist, research software engineer, IT project manager and people manager’ at the STFC’s Scientific Computing Department, based at the Rutherford Appleton Laboratory in Harwell. Through his involvement in the STFC Food Network+ (SFN+) he has added to these roles that of being a Champion for Data Science. Having worked on a phenomenal range of data-based projects over his career– from Internet of Things technologies to the Large Hadron Collider – he provides invaluable expertise to scoping and proof-of-concept projects funded by the SFN+.
He talks about why he became an SFN+ Champion for Data Science; what makes the SFN+ special to him; and the common challenges of data-based projects.

How would you describe your role as an SFN+ Champion for Data Science?
Basically, I seem to have got myself a reputation as the ‘go-to’ data science guy within the SFN+. Through my career, I have developed extensive experience in data science, data security, and data infrastructure. So, I am well used to dealing with the common challenges that any project with a data-based component will encounter.
Almost every SFN+ project has a data-based component, whether this is gathering data to better understand the issues underlying a problem, or applying datasets in new ways – for instance, to develop an overall sustainability metric for different food products. I work with the project teams to develop a secure and user-friendly data architecture.

How many SFN+ projects have you supported so far?
I have honestly lost count! The level of support varies from a very active involvement, such as writing software or data analysis, to offering guidance or supervising a student. But there are common themes across different data-based projects, so after I have developed a solution once it can often be repurposed, for instance, a supply chain management tool that can be adapted for different food products

How did you first become involved with the SFN+?
I originally got involved through a colleague, who wasn’t able to attend the second SFN+ sandpit in March 2019 and suggested I went in his place. I came back having agreed to support five different projects! I hadn’t intended to get so involved all at once. But when people were sitting down in groups to discuss their ideas, I found myself walking around and suggesting things for them to consider, particularly for data analysis. So, one thing led to another… and at the next year’s sandpit, the same thing happened.
 
What sort of skills do you provide?
My core skills are computing, mathematics, and Big Data analysis. I am used to working with data from huge instruments, including the Large Hadron Collider and Square Kilometre Array. A common issue with SFN+ projects is data not being in a standardised form. For example, one project was looking at issues with prawn production overseas (particularly important for the UK since we import nearly all of the prawns we consume) and we were working with various spreadsheets on prawn production. But each one was slightly different from the others, measuring things in slightly different ways. In these situations, you can’t just put everything you have into an analysis tool and process it. Tidying up data can be very time-consuming!
Another way I can support projects is to connect them with other experts, if I am not the right person to help them. Having worked at STFC RAL for over 20 years, I usually know who to ask, whether this is to access a facility, or to advise on a particular type of dataset.

What do you enjoy about supporting SFN+ projects?
Two things stand out in particular. First, the diversity of the community that the SFN+ has brought together – people from different backgrounds, with different skills and abilities. At the sandpit events, for instance, you will typically have chemists, biologists, food scientists, technicians, and data scientists like me, but it just works. I feel there is a very special culture when diverse groups of people physically get together to come up with ideas. I have a lot of happy memories from SFN+ workshops that have involved sketching out ideas on whiteboards and building things with our hands. It is very refreshing in this age when our days are dominated by online meetings.
Another key factor is the network’s global outreach, particularly in Asia and Africa, and the focus on involving local stakeholders. Often, SFN+ projects investigate a highly specific issue with a particular supply chain, so involving local partners enables them to generate tailored evidence. For example, a project could fund a local research assistant to survey farmers in Africa to understand the issues preventing them from selling their produce more effectively on the market. We can then discuss ideas on how to better facilitate the link between suppliers and consumers.
 
Why is the SFN+ important?
I think where the SFN+ really succeeds is by addressing the gap in funding between an idea in someone’s head that may work, and having the proof-of-concept needed to attract lots of investment. Many of the projects I have supported have been able to develop a cheap and cheerful prototype that then acts as a catalyst for further funding. Or they have been able to publish datasets in the public domain that others can then build on. In this respect, the SFN+ really complements the other parts of UKRI.
It also enables small-scale and early-stage projects to access STFC’s world class facilities, such as the Diamond light source and the ISIS neutron and muon source. Finding the right person to talk to and gain access would be much harder without the inside channels that the SFN+ provides.

How successful do you think the SFN+ has been?
I would say that it is an exemplary model for a research network, particularly in terms of productivity. This goes far beyond making physical things: ‘productivity’ can include publications – of papers, code, datasets, etc – presentations at conferences, engaging with food industry companies, growing networks, and making new contacts. Above all, the SFN+ has enabled many proof-of-concept projects to build a persuasive enough case to apply for follow-on funding elsewhere.
Involving PhD students is also an important activity. We make sure that they have a well-defined contribution to the project that they can finish within the timescale of their PhD. This can give them experience of on-the-ground data collection, for instance surveying farmers or other stakeholders in a food supply chain, which is a valuable asset for their CV.

A project supported by a STFC Food Network (SFN) Scoping Project Grant has demonstrated how digital technologies could help create a thriving farmed fish economy in Kenya – supporting livelihoods and increasing access to healthy protein.
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With support from the STFC Food Network+ (SFN+), this project is modelling a future of local, ‘closed-loop’ urban food production systems where nothing goes to waste.
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With funding from an STFC Food Network+ (SFN+) Scoping Grant, an exciting project has shown the potential for computational genetics to detect the most dangerous strains of a notorious food-borne bacteria, Shigatoxigenic E. coli

With support from the STFC Food Network+ (SFN), an interdisciplinary collaboration is breaking new ground in developing low-cost, accurate and autonomous technologies to measure soil properties. 
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