This project will investigate the feasibility of developing an autonomous SmartTraps system for remotely monitoring adult Spotted Wing Drosophila (SWD), an invasive fruit pest. The feasibility of rapidly and accurately identifying the near microscopic Spotted Wing Drosophila males and females by image analysis in a compact, autonomous, non-saturating trap will be determined as the key preliminary step and technological challenge. Other key processes that will need to be developed for a full system beyond a successful outcome of this feasibility study include between trap communication, cloud based data aggregation and analysis, and web and app-based tools to integrate summary data from traps in order to provide decision support for growers. The trap will allow much closer and more reliable monitoring of SWD, and will be a significant advancement in pest specific monitoring technology which will have wide application for numerous pests globally.

More efficient use of inputs including water, fertilisers and pesticides is vital to the future success of all UK agri-businesses. Although over-irrigation and high fertiliser inputs can lead to excessive vegetative growth, increased disease susceptibility, lower marketable yields, poor organoleptic quality and a short shelf-life, many growers are reluctant to reduce water (and fertiliser) inputs due to the lack of information, suitable management tools and crop monitoring systems. Scientifically-derived fertigation strategies have been developed at East Malling Research that improve resource use efficiency, increase marketable yields and fruit quality and reduce waste during production. Scaling-up this precision fertigation approach so that it can be implemented safely across many hectares of high-value substrate strawberries requires a step change in the detail of on-farm measurement data. The project consortium (BerryGardens Growers Ltd, East Malling Research, Delta-T Devices Ltd, Eden Irrigation Consultancy Ltd and the Technology Research Centre Ltd) will develop new technologies needed to implement, monitor and manage precision fertigation across many hectares of high value soft fruit production. Imaging tools to assess plant health, quantify crop quality and predict marketable yields will be developed and validated against conventional but intensive scientific measures of productivity in commercial strawberry varieties exposed to differing degrees of biotic and abiotic stresses. The benefits to the UK horticulture industry will be improved resource use efficiency, reduced pesticide use, improved yield predictions, extended shelf-life and reduced wastage in store and better fruit quality for consumers.

There is increasing consumer and retailer demand for high-quality UK-grown strawberries, and this will increase further post-BREXIT as retailers favour British produce. Currently, c. 30% (Defra) of strawberries consumed in the UK are imported, and so there is a great opportunity to displace these, often inferior, imports during the home-grown season and boost the UK economy. However, achieving consistently high yields and quality across variable and challenging growing seasons is difficult, and new growing innovations are needed if UK fruit production is to be optimised to meet market demand, and imports reduced. There is much variability in plant yield and berry quality over a typical covered table-top production system, but the reasons for this are not fully understood and the magnitude of the effect has not been satisfactorily quantified, and so it cannot be managed or predicted. This variability confounds growers' best efforts to forecast yields to inform marketing strategies, and inaccurate forecasts lead to under-supply or to surpluses, necessitating purchases from outside the UK or fruit destruction, both of which are very costly. We will develop new soft fruit growing strategies from an improved understanding of how to optimise individual plant performance across the growing area. We will refine recently-developed fruit harvest and ripening models by incorporating high resolution, satellite-derived weather inputs, and data feeds will be used to inform growers' polytunnel venting strategies to better control growing conditions. Variable ripening rates and yields will be captured using a new app, and algorithms will be developed and embedded in a cloud-based BerryPredictor tool that will enable growers to forecast yields with much greater accuracy and precision than currently possible, across the entire cropping area. BerryPredictor will also provide POs and UK growers with access to real-time accurate yield prediction profiles for the first time. The majority of the R&D work will be carried out at the NIAB Water Efficient Technologies (WET) Centre, an industry-funded soft fruit precision growing demonstration and KE centre, the remit of which is to showcase the latest innovations in soft fruit growing, and promote the commercialisation of project outputs from our IUK and industry-funded projects. BGG commercial growers will provide weather data to inform the models, and yield data to ground-truth BerryPredictor. Project outputs will benefit UK growers (yield, premium price, import substitution), POs (better product with enhanced reputation, reliability and improved marketability), supermarkets and consumers (flavoursome, phytonutritious UK-grown fruit) and wider society (sustainable intensification).

COVID-19 is creating an existential and widely publicised threat to the otherwise vibrant UK soft fruit sector. The lockdown in March limited access to labour and the UK government launched the Pick for Britain campaign to support the sector as a contingency. However, despite this picking costs have increased, due to the need to train the workforce and some farms have left crops unpicked. Some farms have reported high incidences of COVID-19 outbreaks amongst pickers. There is, therefore, an urgent need to drive labour productivity through automation. This project's objective is to develop and demonstrate a fully automatic blueberry harvesting machine, one of the UK's most important soft fruit crops. The proposed machine is developed from a prior IUK feasibility study (IUK11295). The project will construct a full-scale working machine, including full CAD designs and designed for onward manufacturing. It will be fully electric and include new image analysis systems to optimise crop quality at harvest. The machine removes berries from the bush by the use of innovative shaking systems and can fit inside the small greenhouses and polythene tunnels used by all UK and many EU blueberry producers. Following this project we will have a fully designed machine, which can then be manufactured at scale for widescale deployment by UK growers in the 2021\. This application creates new business opportunities in farm automation but also underpins the economic and environmental sustainability of the soft fruit sector. The industry domain scale is significant; blueberries are now the second largest soft fruit sold in the UK (£337m p.a.). The UK industry has been expanding to meet demand but even so we only have a 7% share of this market. Blueberries are well adapted to the UK summer climate and there is considerable opportunity to grow the UK share. However, the crop requires large amounts of typically migrant labour to pick the fruit, which represents c.40% of production costs. Driving labour productivity in the sector is crucial for a COVID response but also underpins longer term productivity. A more productive UK production base secures environmental sustainability, simply because there will be a reduced requirement to import fruit from overseas producers. The project will be delivered by a well established consortium led by Berry Garden Growers, the UK's largest grower-owned co-operative of soft fruit. In addition the machine will be tested by Lutton Farms, a member of Berry Gardens and the UK's largest grower of blueberries.

The accurate monitoring of pests is vital to profitable crop and livestock production and to human health and optimises pest management through improved interventions. This project will develop a full SmartTraps system for remote autonomous monitoring of Spotted Wing Drosophila (SWD), an invasive soft and stone fruit pest that has increased the cost of production of soft and stone fruit by 10% in most production regions globally including in the UK. The SmartTraps system will rapidly and accurately identify the near microscopic SWD males and females amongst a myriad of non-target species attracted by a broad-spectrum lure, by machine learning image analysis in compact, autonomous, non-saturating (node) traps. The catch data from networks of individual (node) traps will be transmitted via gateway stations to the cloud, with real time cloud-based data integration accessible on the web and a variety of mobile platforms as an aid to scheduling management treatments for the pest. The feasibility of two of the key core processes in this system, automatically capturing images and identifying them, was demonstrated in Innovate UK project 131787 (completed 30 June 2016), an important technological development and advancement in science. The SmartTraps system will be a significant advancement in pest specific monitoring technology with wide application for numerous pests globally, establishing the UK at the leading edge of this new ground breaking technology with huge international market potential with diverse applications in agriculture, health and the environment.