Ensuring food and nutrition security has been a constant struggle throughout human history, but perhaps never more so than now with a rapidly increasing global population (estimated to reach 9.7bn by 2050), the current challenges imposed by the COVID-19 pandemic and the war in Ukraine, and more locally, the legacy effects of BREXIT. Traditional production methods will be unable to meet these challenges, and so new innovation and technologies must be developed to provide food security worldwide - one of the UN Sustainable Development Goals (SDG2). New ways of growing fresh, nutritious, food with an assured shelf-life using fewer inputs and from a smaller area, and in more sustainable and cost-effective ways which circumvent limiting factors such as climate, land-use pressure, and inflationary costs are needed. Total Controlled Environment Agriculture (TCEA) is a promising method of growing plants that is not coupled with weather or land, and could contribute to long-term resilience and self-sufficiency targets. The global vertical farming technology market is valued at £3.12bn, and is estimated to reach £16.77bn by 2027, 23.28% CAGR (Verified Market Research, 2020). Drivers of this growth are the high yield potentials, year-round production, hyper-local production, reduced food-miles and storage requirements, shorter supply chain, use of less water and fertilisers, and minimum agrochemicals. While most vertical farming companies grow leafy greens and salads, there is an opportunity to utilise this technology for other high value horticultural crops such as berries and transplants, larger vegetables and root crops, and plant-based pharmaceuticals and proteins. We will develop a method to produce high quality, virus-and disease-free strawberry plant propagules with assured high cropping potential in TCEA. The resulting pre-programmed, high-health plant material will enable import substitution of both propagules and fruit (currently £40m and £186M per year), reduce chemical inputs and waste (currently £30m/year), and deliver a product that will provide value and security for growers, when planted in conventional polytunnel systems, glasshouses (CEA) or TCEA. To achieve these outputs, we have assembled a multi-disciplinary and collaborative consortium led by Vertical Future (a leading vertical farming technology and research company) with NIAB East Malling (the largest UK research institute conducting applied research in horticulture), the University of Reading, leading strawberry growers Hugh Lowe Farms and Clock House Farm, and their propagation companies (Blaise Plants and Linton Growing, respectively), the leading UK marketing desk Berry Gardens Growers Ltd, Delta-T Devices (agri-tech sensor manufacturer) and Cocogreen (specialist substrate supplier).

Agrivoltaic technology allows dual use of land, combining agricultural production with photovoltaic electricity generation. We have already reported how innovative tinted and semi-transparent solar panels could utilise 'spare' solar irradiation for electricity production when installed above growing plants. Applying these, and newly developed flexible agrivoltaic materials, to existing polytunnels could help UK protected agriculture to meet net-zero carbon targets. This energy-intensive and valuable UK farming sector will: trial how best to install next-generation panels, practically and cost-effectively, in a real-world commercial setting; compare effects on soft fruit of their implementation; and show how generated energy can facilitate automated farming. Applying novel solar panels over existing in-field structures reduces the need for rural land being lost to large solar farms that rarely benefits the grower with clean renewable energy. Here, the power will be used fully on site to grow berry crops using electric power to automate picking, power irrigation, sensing and vehicles; all sourced form renewables primarily, any excess will benefit the national grid in very much the same way a typical solar farm would. Ultimately we wish to grow 100% Electric Berries.

Agrivoltaic technology allows dual use of land, combining agricultural production with photovoltaic electricity generation. We already reported how innovative tinted and semi-transparent solar panels could utilise 'spare' solar irradiation for electricity production when installed above growing plants. Applying these, and newly developed flexible agrivoltaic materials, to existing greenhouses or polytunnels could help UK protected agriculture to meet net-zero carbon targets. This energy-intensive and valuable UK farming sector will: trial how best to install next-generation panels, practically and cost-effectively, in a real-world commercial setting; compare effects on soft fruit of their implementation; and show how generated energy can facilitate automated farming.

World Resources Institute figures suggest that a third of all food produced is wasted, explaining 8% of global greenhouse gas emissions and $940B economic losses per year. This project will focus on waste reduction in strawberry production (average waste 9%, about $1B globally) but the approaches developed will be applicable to other fruit crops too. Picked fruit may be wasted either because it is defective or because it cannot be sold due to market conditions - typically because weather-driven production peaks result in oversupply. Growers try to manage both kinds of waste by surveying the crop (i) to identify causes of lost productivity (diseases, pests, microclimate changes, etc.) and therefore take corrective action and (ii) to predict and therefore better manage the impacts of future yield variation. Current best practice requires experienced harvest managers to 'walk' the crop (often spread over many disparate fields) and attempt a subjective visual assessment of crop health and potential yield for each field. This project will improve yield monitoring and forecasting by using existing soft fruit picking robots to obtain much richer data about the condition of the crop. Denser survey coverage will facilitate the development of more accurate long-range yield forecasting models. Another benefit will be an innovative automatic yield monitoring system sensitive enough to small changes in productivity to provide growers with earlier warning of disease and other causes of waste. This project will be implemented by three exceptionally innovative UK businesses: Dogtooth Technologies, developer of the state-of-the-art soft fruit picking robot, Fresh4cast market leader in the supply of yield forecasting tools, and Hugh Lowe Farms, a widely respected and large UK producer of berry fruits and influential member of the UK's largest soft fruit cooperative Berry Gardens. Following successful completion of this project, the consortium partners will bring to market a yield monitoring/forecasting solution giving step-change performance increase compared to the current state of the art. This will benefit soft fruit producers globally, initially strawberry producers and later producers of other crops.

Raspberries are fragile fruits that require significant manual labour to harvest. The raspberry industry has seen significant growth in production due to consumer demand, but the cost and availability of labour is threatening its economic viability. This project will produce a proof of concept raspberry picking robot that will demonstrate the approach required to alleviate this bottleneck in growth of the sector. Building on the cutting edge developments that Dogtooth Technologies has already achieved in bringing to market a commercial strawberry picking robots, this project will continue to push the boundaries of the application of robotics to the needs of the agricultural sector.

Strawberry harvesting is a labour intensive task that depends critically on the availability of a large amount of low-cost labour. Growers are increasingly vulnerable to labour market price fluctuations and burdened by high employment overheads. Building on Dogtooth's proof of concept strawberry picking robot (developed during Innovate UK project Ananassa), project Vesca will deliver commercially viable picking performance using cutting edge machine learning and computer vision techniques to facilitate more efficient localization of target fruit (by more nearly optimal control of robot motion) and more accurate determination of suitability for picking. The project will also provide ancillary benefits such as yield mapping and prediction that are of significant importance to growers.