SFN Funded Proof of Concept Projects

2022 Funded Projects

We were delighted to award three Proof-of-Concept projects via the SFN Sandpit 2022. We plan to fund up to 8 further Proof of Concept projects in 2022!

Building a Food Waste Tracker for Responsible Consumption and Sustainability Enhancement
​
PI - Dr Shuyang Li (University of Sheffield), 
​Food Side Co-Is: Ifeyinwa Kanu (IntelliDigest), Martin Chadwick (University of Reading), Yang Lu (York St John University)
STFC Side Co-Is: Rebecca Duke

The project aims to develop a food waste tracking application for consumers, retailers, and food manufacturers with the aims of 1) improving the restructure of a Net Zero food system; 2) offering interventions to change consumers' food waste behavior; 3) providing simultaneous analytics for retailers and local manufacturers to make sustainable decisions; 4) improving consumer awareness of healthy diet.

This project extends a previous STFC funded project that developed a ‘World Food Tracker’ app to guide people towards a healthy diet plan and sustainable food consumption. We will enhance the existing functions by adding tracking household food waste, AI-enabled interventions, and simultaneous analytics that provide feedback to supermarkets and food producers.
​

Developing a new testing methodology for honey authentication

PI - Dr Maria Anastasiadi (Cranfield University)
Food Side Co-Is: Dr Zoltan Kevei, Cranfield University
STFC Side Co-Is: Dr Sara Mosca, Prof. Pavel Matousek, Rutherford Appleton Laboratory,

The popularity of honey in the UK is ever increasing leading to large-scale imports from other countries. This trend in combination with the decline of bee populations inevitably leaves the scene open for fraudulent practices, with honey being the third most adulterated food product. Existing methods are not sufficient to detect all types of sugar syrups used for adulteration and at present there is no single method for honey authenticity testing. 

In our previous SFN scoping project, we demonstrated the potential of employing rapid spectroscopic techniques to characterise UK honeys and identify adulteration. Spatially offset Raman spectroscopy (SORS) is a truly non-invasive technique employable in field. Furthermore, molecular techniques have recently shown promising results for the use of DNA markers for botanical authentication and plant-based syrup adulteration detection in honey. 

This project seeks to further investigate the suitability of SORS for adulteration detection and determine parameters such as specificity, sensitivity and accuracy of the method. In addition, we will develop a new method for adulteration detection using qPCR with the aim to discover specific DNA markers for common adulterants such as corn and rice syrup, which could be used in conjunction with SORS to confidently determine the type of adulterant. 

Future Farms: redesigning crop landscapes

PI - Dr Jorg Kaduk (University of Leicester)
STFC Side Co-Is: Dr Jens Jensen (UKRI-STFC), 
Food Side Co-Is: Dr Jake Bishop (University of Reading)
Non-Academic Co-Is: Dr Christopher Nankervis (Weather Logistics Ltd.), Richard Means (Ceres Rural)​

Food systems generate about a third of global annual greenhouse gas (GHG) emissions. More than 70% of those come from land use and change. Climate-conscious management of crop landscapes is therefore central for sustainable land use and net zero GHG emissions. Identifying climate-related opportunities to increase land use sustainability and reduce risk exposure is not only crucial for developing climate resilient strategies, but also to comply with global commitments (e.g. the Paris Agreement) and for businesses to comply with rapidly evolving regulations.

The project develops a user driven multi-peril crop modelling tool. The aims are to 1) address the question of which crops and cultivars to grow where to most sustainably exploit physical climate change opportunities and 2) support users in complying with sustainability and ESG (Environmental, Social, and Governance) requirements. Users could be farmers climate proofing their production, agri-food companies exploring their climate related risks, and consultancies supporting the planning of land use change to insurance companies evaluating risks from crop failure.

The user would specify the crop, and geographical region and time period of interest and the tool determines, for the specified time period, the suitability of the geographical region for the crop, various risks to the crop (e.g. frost, wind damage, drought, and prevalent diseases/pests), water use and carbon emissions as well as sustainability and ESG metrics. The time horizon for the modelling ranges from seasonal forecasts up to 2050. The spatial domain would be initially the UK, but could be expanded to cover continental Europe, and developing countries. The tool will be based on the UK community land surface model JULES and first developed for one crop, e.g. wheat, to demonstrate functionality. Data and model selection will be transparent to the user and the tool will be open to extensions with further models, crops and risks.

Exploring the feasibility of Geographic Indication marketing to improve pastoral livestock marketing in Kenya 
​
​PI - Dr Aditya Parmar (NRI University of Greenwich)
Food Side Co-Is: Delia Randolph (ILRI), 
STFC Side Co-Is: Dr. David Meredith (STFC Hartree Centre), June Po (NRI University of Greenwich)
Non-Academic Cos: Hussein Wario​ (CRDD, Kenya)

People in LMICs prefer meat from smallholder and pastoralist systems and pay a premium for it. But this meat lacks a marketable signature. Geographical indications (GIs) are increasingly seen to support sustainable local development. GIs identify food products that have a specific quality linked to their geographical origin. The qualitative aspects of these food products can be in the form of local resources such as local know-how, cultural traditions, and technical, social, and economic interactions within a territorial food system. These territorial public goods (or terroir) can become a tool for the resilience of the agricultural landscape and growth of the entire region in which a particular agri-food product is produced and processed.

This scoping project explores the possibility of developing GI for the specific process and product quality of sheep and goats produced in pastoral production systems of Marsabit County in Kenya. In Kenya already there is high demand and reputation for the Marsabit sheep and goats for meat and meat-based products. Consumers recognise the geographical name, but no institutional or regulatory framework is available to track the origin and label the meat products as "Marsabit Meat". GI for their products will help the pastoral community to improve profitability and overall income from the sale of small ruminants. Apart from having a significant impact on the income and value addition to the pastoral communities in Marsabit, such a business strategy will support the environment and create conditions for regional competitiveness. A multistakeholder approach will be employed during the project, where the project team will investigate and understand the stakeholder perception regarding establishing GIs for Marsabit sheep and goat meat. In addition, the project will analyse the policy and institutional
incentives by looking at other examples such as "Coffee Kenya", which is one of the successful GI produces from Kenya.

Corn Yield prediction via integration of remote sensing, machine learning and crop modelling 
​
​PI - Vivatvong Vichat-Vadakan (SkyVIV)
STFC Side Co-Is: Dr. Anthony Brown (University of Durham)
​Non-academic Co-Is: Dr. Attachai Jintrawet (Chiang Mai University)

Knowing ahead of time how much crop will be produced at harvest allows us to optimize the processing and supply chain resources needed to get the product from field to consumer. As such, the ability to predict the crop yield ahead is fundamentally important tool with which to reduce the carbon footprint of the agriculture industry.

​Using the Thai corn-industry as a study- case, this project will combine versatile UAV surveying capability with open-source machine learning analysis routines to reveal the hidden markers that accurately predict corn yield. As a ‘value-added’ exercise, to gain more insights into crop health we will create mock crop yield datasets with the DSSAT crop simulation suite and compare to the remote sensing datasets and machine learning results. In particular, these simulated datasets will quantify the crop yields for different variables, such as soil health and weather conditions and as such, these comparisons between observed data and simulated data will allow us to also investigate the crop health and if necessary to take preventative measures as soon as possible, which would further reduce the carbon footprint of the industry.

AI-AMMS: AI coupled with Aerial iMaging and Mobile Sensing

PI - Dr Po Yang (University of Sheffield)
Food Side Co-Is: Dr Daniel Leybourne (ADAS)
STFC Side Co-Is: Dr Melina Zempila (STFC), Dr Michelle Hamilton (STFC)

Winter wheat is one of the most important crops grown over the UK but has an estimated 5-20% of annually average yield lose due to pests and diseases. Pesticides are often applied to crops to provide protection against pest damage and to limit yield losses. These applications are often done on an insurance basis (i.e. a spray is applied as contingency to mitigate potential yield loss) rather than a prescriptive basis because pest abundance is high. With sustainable crop protection becoming more important, there is increasing demand for decision support systems that can help farmers grow more sustainably with fewer chemical inputs and reduced soil erosion.

Existing image-recognition crop pest and diseases monitoring solutions include: 1) Drone image analysis applications are suitable for diseases monitoring in remote large-scale farms, but hardly achieve accurate estimation of pesticide use due to limitations of image spatial resolutions. 2) Mobile image analysis solutions using deep learning techniques enable accurately detecting pests in the fields, but cannot be used in real-world applications due to need of expensive computation resources. 

This project will explore the technical feasibility of integrating UAV and mobile information with advanced multi-scale data fusion techniques into a holistic solution that offers: 1) detection and quantification of wheat diseases in multi-scale fields; 2) measure context of regionally relevant disease tolerance thresholds; 3) determines whether a pesticide application is advised to use. It will build on existing resources held by the University of Sheffield, STFC and other industrial partners, including ADAS, Mutus-Tech Ltd and Velcourt. The technology will potentially deal with wider challenges like reduce growing cost for farmers through reduced pesticide application and be used as a training tool for farmer led IPM. 
​

Evaluating influence of moisture in controlling release of nutrients in novel green fertiliser using neutron imaging and muonic x-rays 
​
PI - Dr Ruben Sakrabani (Cranfield University)
STFC Side Co-Is: Dr Genoveva Burca, Dr Adrian Hillier (ISIS)
Non-academic Co-Is: Mr Alexander Hammond (CCm Technologies Limited)

The increase in carbon dioxide emissions is causing warming of the planet, contributing towards climate change. One way to mitigate the high levels of carbon dioxide in the atmosphere is to capture it into the terrestrial environment. This technology (known as carbon capture – provided by an industrial collaborator) entails capturing carbon dioxide into organic waste material such as food waste, agriculture residues and biosolids. The reactions between the captured carbon dioxide into the organic waste converts it into a renewable source of fertiliser, in the form of pellets. The reliance of such renewable sources of fertilisers is timely as mineral fertilisers have a high energy footprint with some fertiliser companies even stopping their production. This will have direct consequences to food security if no alternative steps are taken. A key challenge in relying on novel fertilisers is assessing how quickly the nutrients in it becomes available to crops as due to variability in feedstock used, this can vary a lot. One factor that influences the release of nutrients from fertilisers is moisture present in soil which is supplied through irrigation or precipitation. 

In this project, STFC facilities will be used to take analyse the physical characteristics (such as dense areas, porosity) and chemical composition within a pellet. The pellets will be artificially exposed to varying amounts of moisture and then corresponding images taken to evaluate changes in physical and chemical status.  The information gathered from this work will inform how the variation in moisture content will influence the physical and chemical composition of the fertiliser pellet. This work will provide fundamental information on nutrient release patterns under varying moisture content which can be linked to crop requirements under varying weather conditions.

Blockchain enabled Cloud Computing based integrated Carbon Calculator (Be4C) 
​
​PI - Dr Sushma Kumari (University of Hull)
​Food Side Co-Is: Dr. Dhaval Thakker (University of Hull), ​Dr. Jyoti Mishra (Leeds University), 
STFC Side Co-Is: Dr. David Meredith (STFC Hartree Centre),
Non-academic CoIs: Shweta Sharma

​
Global warming is an alarming issue for the whole of humanity. The beef supply chain is one of the segments of food industry having considerable carbon footprint throughout its supply chain. The major emissions are occurring at beef farms in the form of methane and nitrous oxide gases. The other carbon hotspots in the beef supply chain are abattoir, processor, logistics and retailer. There is a huge amount of pressure from society and government authorities to all the business firms to cut down carbon emissions. The different stakeholders of beef supply chain especially small and medium-sized stakeholders, lack in technical and financial resources to optimize and measure carbon emissions at their end. Existing carbon calculators help individual beef supply chain stakeholders to understand carbon emission within their firm. Yet, at present there is no system available to calculate and minimize the carbon footprint of entire supply chain and make this information available and visible to all stakeholders in a transparent and secured way.

​In this project, we are planning to develop a Blockchain enabled Cloud Computing based Carbon Calculator where all stakeholders of supply chain can measure, visualize and minimize carbon emission of whole supply chain within reasonable expenses and infrastructure. The integrated approach of mapping the entire beef supply chain by a single cloud will generate transparency, reliability, trust, and security of a record and will help to improve coordination among its stakeholders. The system boundary of this study will be from beef farms to the retailer involving logistics, abattoir, and processor. Proposed system will catalyse a step change in building relationship between technology, decision-making and planning for reducing emissions. It will help multiple stakeholders of beef supply chain to operationalize the net zero strategies and make real changes to their business to achieve net zero carbon emissions by 2050.

Spotting the Rust - Tracking disease progression by static and aerial imagery 
​
​PI - Dr Melina Zempila (STFC RAL Space)
​Food Side Co-Is: Matthew Milner (NIAB)
STFC Side Co-Is: Michelle Hamilton (STFC RAL Space)
Non Academic Co-Is: Anthony Brown (Scientific Remote Sensing)

​
As we all know, wheat is a global commodity, and its production is a key component underpinning worldwide food security. Recently, the rapid global spread of genetically diverse sexually derived Pst races (rust), has resulted in more than 5 million tons of crop loss –and this is a conservative estimation-, this highlights the need for further monitoring of wheat fields with the aim to protect global wheat yields.

Our project proposes a regional monitoring tool that will enable us to timely and accurately identify the emergence and spread of wheat rust, by means of static and aerial imagery. To achieve these goals, we have identified two different but complementary experimental settings:
A) In lab static sampling of healthy and diseased wheat within a controlled environment, with the use of a spectrometer that will provide detailed sample spectra (400 1000nm), and hyperspectral imaging that will enable us to see spatial features over 41 different wavelengths in the visible and short near infrared spectral region.
B) Aerial surveys of wheat plots targeting different rust disease levels, using multispectral (4 user-defined bands) and hyperspectral (41 bands) imaging sensors, allowing us to identify spectral and spatial features alongside with the evolution of the disease.

Acquiring these datasets, will enable us to bridge the gap between in-lab tests and actual field conditions with potential improvements on model libraries, while timely and accurately identification of the emergence and spread of wheat rust, will support us to achieve minimal invasive treatment and thus reducing soil and water contamination. Additionally, the tool will enable end users to adopt time-, cost- and energy-effective approaches towards limiting, controlling, and eliminating rust over their wheat crops, enabling us to reduce our carbon footprint and build sustainable farming practices by supporting crop modelling and decision-making policies over the globe.

AIK Platform - Preservation and visualisation of African Indigenous Knowledge for Resilient Food Systems 2.0 
​
​PI - Dr Steven Sam (Brunel University, London)
​Food Side Co-Is: Mr Sylverster Macauley (Mamie Foundation Sierra Leone)​
STFC Side Co-Is: Dr Ximena Schmidt (Brunel University), Dr Hugh Dickinson (Open University)
Non-Academic Co-Is: Mr Denis Jusu (Jamjay Agricultural Company, Sierra Leone​)

This project builds on a STFC Scoping project and brings together a transdisciplinary team from UK and Sierra Leone to co-design and co-develop an open-access digital knowledge platform. The platform will rescue AIK while promoting and enabling understanding of the positive impact that AIK offers to modern food production and food security, gender equality, climate change, hunger and good health.

African indigenous (AIK) refers to tacit knowledge held in different languages, cultures and skills that drive food production, preservation and consumption for more than 80% of citizens in Africa. The documentation and dissemination of AIK remain a big challenge confronting librarians and other information professionals in Africa, and there is a risk of losing AIK.

There is also a clear disconnect between the AIK and scientific knowledge and modern efforts for sustainable food production. Our project will help promote gender equality and inclusive agricultural development by acknowledging and amplifying women’s roles in the changing agricultural landscape and food production in Sierra Leone. We will pilot, evaluate and validate design functions, user acceptance, effectiveness and impact, and identify contextual and technological challenges with diverse stakeholders applying user- centric and impact assessment research to answer the fundamental research question: What difference can an AIK platform make to managing, sharing and integrating AIK into modern agricultural and food systems practices and policies to improve sustainable, healthy, safe and nutritious food in Sierra Leone?​

---

2021 Funded Projects

The inaugural SFN Call for Proof of Concept projects was held in late 2021 and we were delighted to fund the four projects below (click on the + to learn more about each project).

We plan to fund up to 12 Proof of Concept projects in 2022 so keep an eye our for the call!

Integrating environmental, spatial, and social data to assign food supply chain functions across urban space
​
PI - Daniel Evans (Cranfield University)

Currently, urban food growing takes place in disconnected spaces, and many essential parts of the food supply chain (e.g., collecting waste for composting, forming soil) are displaced to locations beyond the city. There is an increasing demand to grow nutritious food using sustainable and resilient land-use practices which enhance agricultural productivity, support secure and equal access to land, improve land and soil quality, safeguard natural resources, reduce food losses along the supply chain, and decrease waste generation. This requires a new urban food growing system: one which can identify and connect under-utilized urban spaces into a cluster, with each space within the cluster optimized to carry out a specific role in the food supply chain.
This project aims to develop a new decision-making dashboard for urban food growers and land-use planners. The dashboard will be used to identify suitable under-utilized spaces in a city, assess these spaces with respect to environmental, spatial, and social data, and suggest which spaces would be best for specific food supply chain functions. To prove this concept, we will collect environmental, spatial, and social data from a cluster of spaces within the London Borough of Islington. STFC-based data science capabilities (and 2x DISCUS PhD students) will then integrate these data into a model to
produce optimized suggestions of how each space could be used within the cluster. STFC will also lead the production of the prototype dashboard, which will be user-tested by our large, diverse, non-academic team including local food growers/vendors in the Borough (Octopus Communities) as well as policymakers on Islington’s Borough Council. This proof-of-concept project is the first part of a five-phase programme, with future phases including design and construction of the world’s first urban agriculture cluster in London, commercializing the dashboard, and upscaling across Porto, Portugal.

STFC Side Co-I: Pete Hurley (University of Sussex)

From nutrition to flavour: novel food and food ingredients from microalgae

PI - Yixing Sui (University of Greenwich)

Food production with its associated nutritional quality and environmental impact pose major concerns to our society. Microalgae can be a sustainable food source with high nutritional quality, yet its use in food products on the food market are still underexploited. Microalga Dunaliella salina has proven to deliver high value biomass in terms of protein and carotenoids, depending on the cultivation conditions. From a sensory point of view, D. salina biomass also presents a unique taste and odour profile: a sweet taste and floral odour and lacks the more usual umami taste and fishy odour of other algae. Combining its nutritional and taste profiles and ease of processing, it is expected that D. salina biomass will be a beneficial novel ingredient in food products.

This proof-of-concept project aims to assess the effect of different food processing methods on the degradation/conversion/formation of nutritional and taste markers in D. salina, and to achieve the most suitable food product with the right processing condition. The University of Greenwich, The Open University and Minerva will integrate their expertise in microalgal biotechnology, analytical development, and market and communication skills, respectively, to contribute to bringing food ingredients from novel sources into the market and to help develop a positive perception amongst consumers for algae products.

STFC-side Co-Is: Dr. Geraint (Taff) Morgan (The Open University)

The potential of brown rice for improving health: Investigating the bioaccessibility of its key constituents, and barriers and drivers to consumption

PI - Dr Manoj Menon (University of Sheffield)

​Rice is the staple for more than half of the world’s population, providing up to 70% of the energy requirements of many. Over 85% of consumed rice is white rice, nutritionally inferior to brown rice (whole grain or unpolished). Despite the potential of brown rice for improving micronutrient status and blood sugar control, its uptake is generally low. Some brown rice types may also contain inorganic arsenic (iAs), a group 1 carcinogen. To confirm these benefits and risks, we need to evaluate the bioaccessibility (the amount available for assimilation) of micronutrients and iAs from brown rice types as there have been no previous studies. Concurrently, identifying barriers and drivers to brown rice consumption is essential for informing public health strategies. The objectives of this project are the following:

Measure bioaccessibility of micronutrients and iAs and the impact of phytic acid on nutrient bioaccessibility in brown rice using an in vitro simulated human digestion model.
Determine rice type preferences, perceptions, barriers and drivers to consumption amongst adult type 2 diabetes patients living in Chennai, India 
Quantify the risks and benefits of consuming brown rice using the data obtained from the above two objectives.
Co-create new research proposals, publications and disseminate findings to patients, the public and academia. 

The project team involves academics from the UK (University of Sheffield, Diamond Light Source) and two non-academic partners from India (SAMARTH-NGO and MDRF-Madras Diabetic Research Foundation).  

STFC Side Co-Is: Dr Tina Geraki (STFC Diamond Light Source)
Food Side Co-Is: Dr Viren Ranawana (University of Sheffield)

​Monitoring tropical pollinators in conventional and organic fruit orchards year-round in central Thailand

PI - Dr Alyssa Stewart (Mahidol University Thailand)

Most plants depend on animals to pollinate their flowers, including many agricultural crops. The most well-known pollinators are honey bees, but many other insects pollinate flowers as well, including carpenter bees, sweat bees, stingless bees, wasps, butterflies, and flower flies. However, insect pollinators are currently experiencing many threats, including the widespread use of chemical pesticides.

​Our research aims to compare pollinator communities in conventional and organic fruit orchards in central Thailand. Moreover, data will be collected across different seasons (rainy and dry), as both pollinator composition and the impact of pesticides on pollinators may vary seasonally. To do so, we will use time-lapse photography and computer vision to automatically detect and identify native pollinators in conventional and organic guava farms. These data will enable us to quickly and efficiently assess how orchard management impacts pollinator diversity and abundance. Moreover, the results of this project will be valuable at multiple scales, from demonstrating the benefits of organic farming practices to local farmers, to providing data that can guide nation-wide and international policy and conservation efforts.

STFC Side Co-Is: Dr Jens Jensen (STFC)
Food Side Co-Is: Dr. Maria Anastasiadi (Cranfield University)

​