Projects Awarded in our First Funding Round

We were bowled over by the response of 34 applications to our Collaborative Scoping Call in early 2018. This was more than 3 times what we had profiled to spend at this stage so we are delighted that we have been able to award so many of these (including the 2 awarded at our February Sandpit event) thanks in part to an additional £100k award from the STFC.

You can find out all about our successful applications here: 

Piloting Zooniverse to help us understand citizen food perceptions
PI - Christian Reynolds (University of Sheffield)
Food Side Co-Investigators -  Luca Panzone (University of Newcastle), Ximena Schmidt  (University of Manchester), Astrid Kause (University of Leeds), Charles Ffoulkes (ADAS)
STFC Side Co-Investigators - Chris Lintott (Zooniverse, Oxford University), Steohen Serjeant (Open University), Changqiong Wang (University of Reading), Coleman Krawczyk (University of Portsmouth)

There is a food knowledge disconnect between the food research community, and general population. Food experts know detailed information about foods, but we do not know, (and cannot measure easily) what citizens understand or perceive to know about food. This pilot will use the STFC funded Zooniverse platform to ask citizens to provide their perceptions about images of specific foods (and serving sizes). For each image, one of a range of questions will be asked including perceptions of greenhouse gas emissions and energy (calorie content). We currently have 2 food image banks from prior projects (Intake24, and NU-Food). These will be uploaded to Zooniverse and used as the pilot classification dataset. Results will be analysed using Bayesian statistics, after seeking advice from STFC data scientists.

​Data-driven agri-food supply chains for sustainability and productivity: A case in Henan Province, China
PI - Wantao Yu (University of Roehampton)
Food Side Co-Investigator - Sonal Choudhary (University of Sheffield)
​STFC Side Co-Investigators - Tom Kirkham (STFC Hartree Centre), Tom Collingwood (STFC Hartree Centre)

​This project aims to improve sustainability and productivity of agri-food supply chains by promoting established good practices in Big Data, Internet of Things (IoT) and Blockchain applications. STFC brings its expertise in all three of these areas to this project. This project will predominantly focus on Henan Province, known as “the breadbasket of China”. Recently completed Agri-Tech in China: Newton Network+ (ATCNN) projects show that agri-food enterprises in China have insufficient understanding and knowledge about how IoT and Blockchain applications and collecting, organising and analysing Big Data help manage sustainable food production. To address the knowledge gap, by working closely with the Chinese partners, the project will first identify the best practices in the UK and China using focus group discussion, expert interviews and secondary data analysis, and secondly, facilitate the knowledge transfer through a dissemination workshop.

​Application of Cryogenics to Optimise Cold Supply Chains for Agri-food products in India
PI - Sonal Choudhary (University of Sheffield)
Food Side Co-Investigators - Rakesh Nayak (University of Hull), Sanjay Lanka (University of Sheffield)
STFC Side Co-Investigators - John Vandore (STFC - RAL), Bryan Shaughnessy (STFC - RAL)

Our project aims to explore capabilities and potential of STFC Cryogenic and Thermal Modelling for reducing food and energy losses in the supply chain, in places where the infrastructure is not well developed - such as in developing countries (India in this case). Unavailability of appropriate technological knowhow, infrastructure and knowledge results in 90% of food wastage in supply chain of developing countries, most of which are associated with fresh food. Advanced technologies such as Cryogenics have potential to strengthen the fresh-food supply by reducing post harvest losses from production-to-consumption level and reduce energy requirements compared to traditional cold supply chains. Using STFC’s proven expertise in Cryogenics, this project would study the implementation challenges, market and knowledge gap, infrastructure and thermal requirement in India from multi-stakeholders perspective: farmers, food processors, distributors, retailers (Amazon India, Tata Tesco and Reliance Fresh), industry gas companies, infrastructure development company, NGOs and government organisations.

​Combining IR and pattern recognition to enhance pregnancy success in cattle: Sensing well done steak.
​PI - Niamh Forde (University of Leeds)
STFC Side Co-Investigators - Anthony Brown (Durham University), Stephen Serjeant (Open University)

This project will apply pattern recognition techniques, used to analyse astronomical data, to drone-based infrared imaging technology to rapidly survey livestock, spread over a large area, and identify female cows to be inseminated. We will capitalise on the STFC drone expertise for this project. We will look to quantify the accuracy the technique by calibrating the infrared sensors, the accuracy of the automated pattern recognition software, and ultimately, the accuracy of the technique by training against a control sample of cows that are near ovulation. Our proof-of-concept test will be a blind test, with only the PI knowing which cow is near ovulation, and the research Co-I's identifying the cow with drone based imaging.

Scoping the feasibility of low-cost GC and GCxGC platforms for using volatile organic compound markers to assess quality of fresh fruit and vegetables throughout the supply chain.
PI - Hilary Rogers (Cardiff University)
Food Side Co-Investigator - Carsten Müller​ (Cardiff University)
STFC Side Co-Investigators - Geraint Morgan (AST Solutions and Open University), Simon Sheridan (AST Solutions and Open University)

Minimally processed fresh fruit and vegetables represent a growing market, providing and enhancing access to fresh produce. However these products have a very short shelf-life resulting in a rapid reduction in nutritional value and high waste. Thus there is a need to develop rapid and cost-effective quality assessment for the industry. We have identified markers based on volatile organic compounds (VOCs), analyzed by thermal desorption gas chromatography- time-of-flight mass spectrometry (TD-GC-ToF-MS), to assess shelf life and effects of processing. The challenge now is to transfer this technology onto a cost-effective platform to allow application at different points in the supply chain from intake to retail. We will explore whether STFC- expertise in bespoke instrumentation development for planetary exploration (Rosetta, Beagle2, LUNA27) can be applied to detect our markers and to develop affordable, portable instruments and rapid data analysis.

Applying the STFC Central Laser Facility towards a characterising a Proof-of-Concept Biomimetic “Sentinel” Sensor System for Crop Fungal & Viral Diseases
PI - Bruce Grieve (University of Manchester)
​STFC Side Co-Investigator - Dave Clarke (STFC)

The project will use the STFC Central Laser Facility (CLF) to enable the demonstration of a laboratory proof-of-concept for a stereo-printed sensor surface capable of detecting air-borne (aerosol or insect) viral and fungal disease vectors, and characterise this system, using CLF expertise and assets, for one disease in a manner suitable for translation into a real-time crop risk sensor. The latter will be suitable for integration with wireless in-field and handheld units (phone- connected or integrated), at an appropriate cost and robustness for wide-scale deployment in Sub-Saharan Africa as well as developed farming systems in Europe and North America.

Arsenic Detection and Distribution in Rice Plants Using High Resolution X-ray Imaging
PI - Manoj Menon  (University of Sheffield)
​Food Side Co-Investigators - Christian Reynolds  (University of Sheffield), Maria Romero Gonzalez (University of Sheffield), Binoy Sarkar (University of Sheffield) Edward Rhodes  (University of Sheffield), Natasha Falconer  (University of Aberdeen)
​STFC Side Co-Investigators - Sarah Rogers (STFC), Fred Mosselmans (STFC), Claire Pizzey (STFC)

Rice is one of the widely consumed cereal crops in the world. However, likely presence of Arsenic (As) in rice poses a significant health risk when it is grown in an As-rich environment. Rice cultivars vary in their uptake and accumulation of As in grains. Less understood, however, is its spatial distribution and accumulation patterns in different plant parts in cultivars. Arsenic accumulation in other aerial plant parts is a significant concern as both husk and rice straw are widely used for feeding ruminants, extending the risks through consumption of meat and dairy products. The project aims to study the spatial distribution of As in rice plants using x-ray imaging (fluorescence and tomography) facilities at Diamond. This cross-disciplinary project will perform preliminary investigations using a selected number of samples at a very high resolution (2μm). The team aims to produce much-needed research evidence towards future grant applications and publications.

Identifying and measuring individual crop areas in small fields in satellite data, moving to yield prediction and growth interventions
PI - Pat Heslop-Harrison (University of Leicester)
Food Side Co-Investigators - Trude Schwarzacher (University of Leicester), Paul Wilkin (Royal Botanic Gardens Kew), James Borrell (Royal Botanic Gardens Kew), Worku Mhiret (University of Gondar, Ethiopia) Universities of Addis Ababa and Hawassa
​STFC Side Co-Investigators - Louise Butcher (STFC Hartree), Philip Rooney (University of Sussex)

We will initiate proof of principle work using earth observation data from Sentinel-2 and MODIS satellites, addressing “What crop is being grown in each field?”. Beyond this, we will integrate health (disease and nutritional status), maturity, water and temperature (latter well known from weather data) at field-plot-level to predict harvest and crop production, predicting interventions required to improve agronomy. These data are critical for food security and planning, having major impacts on social and economic development and health (most Sustainable Development Goals). The scoping grant will enable 10 days of STFC work to underpin applications for larger grants involving STFC researchers and data scientists with crop biologists and ecologists. Funds will be used with our Ethiopian collaborators to collect local data from typical small plots. The question is far from new but 50-years of satellite earth observation has failed to move beyond regional scale and generalized models such as NDVI.

The use of aroma volatiles profile for detecting mesocarp disorders in avocado fruit
​PI - Marcin Glowacz (Natural Resources Institute, University of Greenwich)
STFC Side Co-Investigators - Geraint Morgan (AST Solutions and Open University), Simon Sheridan (AST Solutions and Open University)

​The avocado fruit is prone to developing various internal disorder  (e.g. vascular browning, grey pulp, fungal decay, etc.) which are not visible from the outside. Thus, the purpose of this research is to investigate the feasibility of using thermal desorption gas chromatography - mass spectrometry (TD-GC-MS) system for determining aroma volatile organic compounds (VOCs) profile. It would inform us whether this system can be used for detecting mesocarp disorders in avocado fruit - reducing the number of poor quality fruit within the supply chain, in this way decreasing the likelihood of consumers’ complaints and thus increasing their satisfaction. This scoping project combines the knowledge of avocado fruit physiology with the specialized expertise in the collection, separation and identification of volatile organic compounds (VOCs) in remote locations, as exemplified by the successful development and application of the Ptolemy instrument, after a 10 year and 4 billion mile journey, for the comet chasing Rosetta mission.

​STRIMER - STrawberry Ripeness Identification by MicrowavE Resonance
​PI - Fumie Costen (University of Manchester)
STFC Side Co-Investigator - Brian Ellison (STFC)

Soft fruit produce is a highly productive and competitive sector of UK agricultural industry. It is worth more than £1.2B/year to the economy and provides extensive employment. Of this, approximately 70% is derived from strawberry production; a highly seasonal group with a short and critical harvesting period. Determining optimum strawberry ripeness is essential in maximising crop yield and quality, which increases profit and export competitiveness. The University of Manchester(UoM), with the support of the STFC, proposes to develop a novel strawberry ripeness indicator that uses a microwave resonance technique to measure growth- related variation in the fruit complex permittivity. Manchester and STFC possess advanced microwave software modelling tools, instrumentation and expertise to support preliminary simulation and proof-of-concept experimental work. A ripeness estimation algorithm will be generated and a future portable system conceived that, when developed, will provide fruit farmers with a new agricultural tool.

Influencing phosphorus speciation in Soil-Root environment for reduced phosphorus accumulation and sustainable global food production [InRoot]
PI - Devendra Saroj (University of Surrey)
STFC Side Co-Investigator - Anthony Parker (STFC Central Laser Facility)

The phosphorus (P) leaching from agricultural runoff and P pollution from wastewater results in eutrophication of freshwater bodies. A significant progress has been made during last few decades on the recycling of phosphorus in agriculture to reduce the eutrophication of freshwater bodies and reduce the fertiliser demand. However, the demand for phosphorus mining continues to grow due to soil P accumulation. In order to make a step change in the demand for phosphorus mining, the problem of soil P accumulation must be addressed, besides the recycling of phosphorus as fertiliser. This project aims at developing a new understanding of P accumulation and speciation on soil and plant root environment. As a first approach, we seek to develop quantification of P levels in soil and will seek to use STFC's Raman spectroscopy expertise to determine retention times following treatment of a variety of soil systems under a range of simulated wetting conditions.

Through-container detection and quantification of the adulteration of fruit juices and coconut water using handheld spatially offset Raman scattering
PI - Roy Goodacre (University of Manchester)
Food Side Co-Investigator - David Ellis (University of Manchester)
STFC Side Co-Investigator - Pavel Matousek (STFC)

The project is to develop and test the ability of spatially offset Raman spectroscopy (SORS), coupled with robust multivariate data analysis, for assuring the integrity of food. We have chosen fruit juices and coconut water as potential foodstuffs that are susceptible to adulteration. Both products are popular in the UK (and indeed worldwide), of high value and we have worked with these drinks before. All applicants have experience of SORS for through container counterfeit detection in alcoholic drinks. We consider that SORS has the potential to be a highly disruptive technology for food integrity analysis and could be a very valuable capable guardian (detection technology) within food systems. Both Manchester and STFC have SORS instruments so this would also allow for inter-laboratory comparisons which are needed for robust analyses across many laboratories.

​Remote sensing of soil water content.
​PI - Anthony Brown  (Durham University)
Food Side Co-Investigator - Karen Rial-Lovera (Royal Agricultural University)
STFC Side Co-Investigator - Genoveva Burca (STFC - RAL)

Knowing the amount of water that is present in the soil is fundamentally important to food production. Too little or too much water and the crops don’t grow properly, if at all. Furthermore, saturated ground increases the likelihood of water run-off carrying away important nutrients and fertilisers. This project will look to produce a drone-based prototype to remotely sense the presence of water, using a variety of pass-band filters for optical/infrared light, to concentrate on key water absorption features in reflected light. Comparing the amount of light in these different pass-bands allows us to sense the presence of water. One of the key aspects of this project will be the attention given to calibrating the optical/infrared detectors which draws on our previous STFC-funded work on using these detectors for telescope calibration. Using well calibrated light sources, we will investigate how accurate off-the-shelf cameras are. Using off-the-shelf components will allow the technique to be accessible to the wider farming community then a select few.

Remote monitoring of soil loosening impacts on improved grasslands
​PI - Guy Ziv (University of Leeds)
Food Side Co-Investigator - Andy Hooper (University of Leeds)
STFC Side Co-Investigators - Simon Pearson (University of Lincoln), Jonathan Evans (Centre for Ecology and Hydrology), George Petropoulos (Department of Mineral Resources Engineering, Technical University of Crete)

​Monitoring the impact of mechanical soil loosening on grass yields and soil moisture dynamics is key to provide evidence-based advice to dairy farmers. Current knowledge is sparse and sometimes conflicting, and the experimentation required is difficult to scale up. Using a split- field sub-soil commercial farm field study near Penrith in Cumbria, we will test the feasibility of measuring productivity and hydrological functions, employing a state-of-art in-situ stationary and robotic mobile multi-sensor platform for the calibration of soil moisture remote-sensing algorithms combining stationary and autonomous mobile platform COSMOS sensors.
​

​Exploring novel techniques to assess food price shocks
​PI - Aled Jones (Anglia Ruskin University)
Food Side Co-Investigator -Valeria Shumaylova (University of Cambridge)

​Food systems represent a significant risk to financial and political stability in a number of regions around the world. The Global Sustainability Institute at Anglia Ruskin University has been building models, gathering data and developing methods to explore the dynamics involved in civil unrest, financial instability and local responses associated with food production shocks. These include agent based modelling, systems dynamic modelling, narratives, scenario development, access to weather systems monitoring and war gaming. However, the analysis techniques applied to understanding historic food price dynamics as a result of production shocks are simple econometric tools such as regression testing. A much more sophisticated approach to data analysis could yield new insights in historic price shocks that would better inform policy and market based responses. By working with STFC expertise from the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge this project will explore some of those techniques.

Hacking the second green revolution
​PI - Paul Scholefield (NERC Centre for Ecology and Hydrology)
Food Side Co-Investigators - Toby Waine (Cranfield University)
STFC Side Co-Investigators - Yonghuai Liu (Aberystwyth University), Rene Breton (University of Manchester), Joseph Fennell (University of Manchester), Hugh Mortimer (STFC)

​Understanding crop yield prediction and crop productivity requires a scaling of measurement and monitoring for a global approach. While remote sensing techniques provide high temporal resolution and broad coverage, the hyperspectral imaging provides data to distinguish the growth conditions and species of plants. Here the key issue is how to combine these two kinds of data for such purposes of plant growth monitoring and yield prediction at high spatial resolution such as in small scale crop production. While each has its own characteristics,this project intends to bring STFC data scientists together to analyse both hyperspectral and LIDAR data to calibrate plant canopy reflectance. In return, the reflection properties can be used to predict the plant growth conditions and ultimately yield. Such projects involve several stages of data fusion and analysis such as data capture, noise removal/data normalization, data modeling, model testing and improvement. Various tools and methods will be implemented and developed for noise removal, data normalization, 3D modeling, visualization, skeletonization, trait measurement, association of light reflection and plant growth conditions and yield, and for the statistical analysis of the effects of different features within the models. New data will be collected in due course for testing during model development. The research findings will be presented in international conferences and workshops and published in the international journals.

Scoping the possibilities for Multi-modal sensing for non-destructive assessment of avocado fruit quality
PI - Mike O'Toole (University of Manchester)
Food Side Co-Investigator - Marcin Glowacz (Natural Resources Institute, University of Greenwich)
STFC Side Co-Investigator - Hugh Mortimer (STFC)

This project will look into the investigation of new sensor technologies for non-destructive measurement of avocado fruit maturity. Avocado fruit is a high-value fruit of growing popularity among consumers. However, retailers have noticed that supplied batches/cartons have considerable variation in maturity. Furthermore, it has become clear that the traditional method of gauging ripeness – by colour change – has proven unreliable. This has prompted complaints from consumers, and poses a problem for industry, who are seeking a consistent and accurate method for measuring fruit maturity. We aim to investigate two sensor modalities: (1) hyper-spectral imaging of the fruit using new sensors originally developed for planetary observation, and (2) a magnetic induction method which uses bio-impedance spectroscopy to obtain information about the cell properties of a bulk biological sample. This method has been shown to be capable of robust measurement of various agricultural produce, such as apples, potatoes, pears, amongst others.

Internet of Things devices in agriculture.
​PI - ​S M Shariar Morshed Rajib (University of Manchester)
Food Side ​Co-Investigator - Bruce Grieve (University of Manchester)
STFC Side Co-Investigator - Tom Kirkham (STFC)

​The ability to securely and reliably monitor agricultural farms is vital to Precision Farming and increasing yields. The standard in agriculture is to use sensing devices that operate locally and log data using wired systems or wireless protocols, for example, WiFi, Bluetooth, etc. These methods have several limitations such as high-cost, low-range, and greater power consumption. These limitations impede the system from being flexible, scalable, and convenient. The aim of this research is to exploit Long Range Wide Area Network (LoRaWAN) technology to create an Internet of Things based platform that can integrate low-cost sensors used in agricultural sector, and apply cloud computing and statistical analysis to improve accuracy of the sensors. In the process we will create a low-cost, flexible and scalable Intelligent Monitoring System for Agricultural duties. This project will utilise STFC Data Science capabilities to sift through the data generated by the sensors and analyse it meaningfully. 

​Continuous Ammonia Monitoring for AGriculture - CAMAG
​PI - Brian Ellison (STFC)
​Food Side ​Co-Investigators - Lizzie Sagoo (ADAS), Fangjie Zhao (Rothamsted)

Ammonia (NH3) is an atmospheric pollutant of international environmental concern. Its release into the atmosphere is predominantly associated with agricultural use; particularly from livestock where, for example, it is lost from grazing, housing, hard-standings, manure storage and land spreading. Within the UK, about 80% of agricultural ammonia emissions are also from livestock, with the remaining 20% from mineral fertilizer application. International targets aimed at achieving emission reduction have therefore been established and methods of abatement relating to, for example, optimizing slurry application, incorporation and storage have been identified. Ensuring adequate abatement requires precise detection and continuous monitoring of NH3. To achieve this, the CAMAG project will both apply and explore an advanced gas sensing technique, originally developed for radio astronomy research, that detects the natural microwave spectral emission signature of NH3. The accuracy of the methodology will be determined and assessed, and its performance compared with alternative sensing methods. 

Investigating the nano to microstructural architecture of ingredients and intermediates to improve industrial snack product performance
​​PI - ​Bruce Linter (PepsiCo International)
Food Side ​Co-Investigators - Bhavnita Patel (PepsiCo), David Jones (PepsiCo), Amanda Talhat (PepsiCo), John Bows (PepsiCo) Ian Hamilton (PepsiCo), Tim Ingmire  (PepsiCo), Stacie Tibos  (PepsiCo)
STFC Side Co-Investigators - Tom Kirkham (STFC Hartree) Genoveva Burca (STFC - ISIS), Dave Clarke (STFC - CLS), Kathryn Welsby (STFC - CLS), Claire Pizzey (STFC - DLS) Sally Irvine (STFC - DLS), Lee Connor (STFC - DLS)

Food and drink is the largest manufacturing sector in the UK. PepsiCo is among the biggest businesses in the sector, both nationally and globally. Despite the use of  identical ingredients and manufacturing, we still experience varied consumer perceptions of our product textures across our markets. This suggests not only a requirement for further development of our specifications, but also a deeper microstructural understanding of our ingredients. Furthermore, we need to improve our understanding of the expansion of our products, particularly crisps and crackers.  This funding will be used to scope a future PepsiCo-STFC project, with the aim of investigating the microstructure and material science of the ingredients, aspects which are typically not included in material specifications to determine new ingredients. The funding requested will be used to host a facilitated technical workshop with leading academics, PepsiCo employees and key members of the STFC. These, in turn, will be used to propose technical work packages for projects via different routes of funding such as Bridging for Innovation.

This is not yet the complete list - we'll be updating over the coming weeks as we work through details with our applicants

​Page Header Image: An example of simulated data modelled for the CMS detector on the Large Hadron Collider (LHC) at CERN -  Source: Lucas Taylor for CERN http://cdsweb.cern.ch/record/628469