The use of machines to automate previously manual tasks has revolutionised many aspects of agriculture, such as seed planting, weeding and harvesting of some fruits and vegetables. However, many jobs in agriculture are still manual, despite their apparent simplicity, largely because of a combination of the programming/set-up costs for robots to do them are prohibitively expensive, or these deceptively simple tasks are surprisingly difficult for robotics. Robots are good at executing repeated tasks with identical objects. Many agricultural tasks are varied, however, involving irregular objects in irregular and/or non-uniform manipulations that require feedback from the environment and a basic "understanding" of the task to be completed. While straightforward for humans, such tasks remain largely out of reach for robots. Lack of automation limits productivity for the agriculture sector, which is compounded by chronic labour shortages for low-skilled repetitive manual work, and contributes to food waste. In this project, we will build on recent developments in Artificial Intelligence (AI) and Machine learning (ML) to develop low-cost robotics solutions for a number of currently hard-to-automate agricultural tasks. We aim to drastically reduce the time and cost to set-up robotics installations, as well as improve accuracy and reliability. As a demonstration of this new technology, two processes will be taught to the robot - processing celeriac (removal of leafy top, unwanted roots and imperfections) and replanting of germinated pak choi (picking small pak choi growing in ellepots and moving it to be planted out in the glass house). These tasks were identified by our project partner, M&W Mack, as being ones where automation is urgently needed. Our robotics solution will be adaptable to a wide range of currently manual agricultural processing jobs, leading to savings for food producers and processors, reduced waste and lower prices for consumers. Our existing relationships with large farming and food-companies will allow us to bring our innovation into the market quickly, creating significant revenue and jobs for our company, and helping the agricultural sector to improve the efficiency and productivity of many vital processing tasks.

Our project will demonstrate the feasibility of a new robot motion planning algorithm that will increase the productivity of industrial robots. We will demonstrate the algorithm on a robot packing fruit and vegetables. The technology can be applied much more broadly to help manufacturers reduce costs and the CO2 footprint of their industrial robots.

English Growers' ability to obtain the large seasonal (75,000 strong)(5) workforce to harvest field vegetables has been severely impacted by Covid-19, Brexit and declining migrant worker availability across Europe(10). Domestic labour ('Pick for Britain') cannot replace the need(11). As price-takers with low margins, growers cannot profitably offer higher wages(12). This has become a national crisis that threatens the UK food system's resilience more than any other single challenge(60). **"Unprecedented labour shortages have left hundreds of tonnes of produce rotting in the fields"** Financial Times(57,22) **"Labour availability is very tight \[...\] we could have done extra volume \[with more workers\]" (G. Read, Staples Vegetables UK)** **"If we can't get enough people, ...we don't have a business! \[...\] Imports carry a higher carbon footprint and we cannot fully control provenance and growing practices."** (John Chinn, Cobrey Farms) "Labour shortage for harvesting is our greatest current concern, forcing us to significantly reduce our future cultivation of crops like courgettes. Harvest automation is the only viable solution for our profitability, keep operating in England, and **2025 Net Zero aims(14)**''. Barfoots of Botley, major UK Courgette and Asparagus grower. A 30% **shortfall of harvest workers in 2020(13) resulting in wasted crops has been attributed to Covid-19**; Brexit and **the** narrowing wage-gap between Eastern Europe and the UK (BBC Farming today 26.10.2021: Monthly labour costs in Romania have tripled from £300 to £1000); The Association of Labour Providers: **"staff shortages in the food supply chain are unprecedented, bleak, and ongoing. 99% of labour providers couldn't meet needs for workers in the last 3 months, and 75% will not be able to meet demand in the run-up to Christmas".(**61**)** MM's agri-robot, Sprout, is a lightweight (doesn't compact soil), battery-operated (sustainable) robot that selectively harvests vegetables in-field. It has already been demonstrated to selectively-harvest asparagus in-field with a greater yield than several labourers. Despite these successes, a single Sprout cannot selectively harvest a typical crop alone! A **herd of semi-autonomous Sprouts is needed**. This represents MM's next significant innovation challenge, realising a herd (swarm) of robots. This £1.7m, 19-month collaborative project between MM and its Grower Collaborators will develop and demonstrate a working herd of harvesting Agri-robots able to harvest vegetables in-field sustainably and reliably. It will overcome challenges in safety, harvest-planning, communication and display, on-farm infrastructure; transporting the herd between locations and designing for further reduction in mass and further-improved yield.

The food retail industry is experiencing increasing demand from consumers for UK grown fresh produce and would like to substitute imports with home produce. The demand for home grown plums cannot currently be met due to unreliable and inefficient cropping systems. This collaborative project will develop integrated new technologies that will address the major existing production problems and limitations for fresh plums. The sustainable intensification of this horticultural crop will be achieved through integration of a high-density growing system with new rootstocks, varieties and manipulation of tree architecture for increased yield, coupled with protected cropping regimes and component technologies that will regulate crop load, fruit ripening and give significant season extension. This intensive and profitable growing system will enable UK growers to confidently invest in plum production, delivering substantial economic impact (>£10 m/yr) to the UK horticulture industry.

This project brings together the University of Leeds and Grantham Institute with commercial developers from BioCarbon Tracker and ESRI UK to provide Sainsbury’s with insights into site-specific, multi-parameter environmental risk profiles, both for now and for future forecasting. Risk profiles will combine three categories of data: multiple layers of geospatial data for environmental conditions; combined with crop models and production conditions (to predict performance); and linked to corporate data (such as supplier locations, supply chains, procurement volumes). Outputs from geoprocess models will indicate risks - by type and location - that affect product quality, availability or cost and allow model-based analysis of sustainability and risk. Within the project, ArcGIS Online will be used to share findings with Sainsbury’s suppliers for specific sites, which will be validated with growers’ direct experiences. The development of this system will provide insights to Sainsbury’s and its vast network of suppliers to make them more competitive in the global market. Insights will ultimately inform investment decisions and contractual arrangements to shape supply chains of the future.

Plums and cherries are among the favourite fruits of the consumer, but currently much of the demand is met by imported fruit. With new plum and cherry varieties and new production techniques the UK industry has the potential to increase production. However, post-harvest losses can be significant due to fungal rots and rapid loss of fruit quality. The objective of this project is to develop improved methods of managing the fruit post-harvest based on cold storage and the use of post-harvest biocontrol treatments to minimise losses due to fungal rots and to maintain fruit quality. These new control strategies post-harvest will enable growers and distributors to reduce fruit waste, extend product shelf-life, extend the marketing period and provide UK-consumers with first quality, locally produced plums and cherries for longer.

The aim of the project it to reduce and ultimately eliminate labelling, date coding and packaging mistakes caused by human error in the produce and wider food supply chain. Each year many millions of pounds of produce is disposed of before it reaches the customer because it cannot be sold due to the information or date code on the pack being incorrect, which could lead to either safety, quality or legality issues. This will be achieved by understanding what causes errors and developing a system that ensure these stimuli are removed from the environment to encourage accuracy. The ultimate aim is that the learning of this project will be applied to all food production environments and potentially to other manufacturing operations.

MACK Multiples Ltd. and Onnic International Ltd. have joined forces to develop an ozone generating, point delivery system that will work effectively, in container shepments of fresh produce, to reduce ethylene promoted and microbial spoilage waste. The interval between loading and discharge can range from 20 to 50 days, with the shipments subject to levels of waste during transit that can exceed 10%. This project is designed to allow the refined development of a small, reusable, ozone generating device that will provide a programmable product specific point delivery levels of this gas withing the distribution outers of the palletised loads of fresh produce within the shipping containers that will sustantially reduce these wastage levels The project will run over a two-year period with an output designed to offer a commercially viable, maintained quality, extended shelf-life system to importers, packers, retailers and consumers.

This project aims to improve commercial practices in maintaining produce quality and reducing waste within the food supply chain. A novel environmentally friendly antimicrobial solution will be developed and delivered using unique fogging and spraying technology. These solutions will be used to wash, fog and/or spray fresh fruit and vegetables to reduce the number of microorganisms that could cause spoilage (reducing food wastage). This approach will also help reduce the presence of microorganisms capable of causing foodborne diseases, increasing food safety. The delivered antimicrobial solutions ultimately revert to salt and water, and so are compatible with wide-spread use as a surface disinfectant within industry processes. Collectively the use of this technology to control food associated microorganisms will result in significant positive impacts on the food supply chain.