The Technology Strategy Board has teamed up with other UK public sector funding bodies to support over thirty projects put forward by business led consortia from across agriculture and the crop protection industry, at a total cost of £13.5 million.
The projects will develop technologies to help farmers and growers comply with recent changes to EU pesticide regulations, which will see a number of crop protection products withdrawn. At the same time the projects support the broader aims of the Technology Strategy Board’s Sustainable Agriculture and Food Innovation programme.
Taking into account contributions from the companies that are taking part, the total value of the R&D is in excess of £25 million. The thirty two consortia will bring together over 100 companies, research establishments and other organisations.
Technology Strategy Board Chief Executive Iain Gray said the grants are the first made under the Sustainable Agriculture and Food Innovation Platform, which aims to bring government, business and researchers together to stimulate the development of new technologies that will increase food productivity, while decreasing the environmental impact of the food and farming industries.
The Sustainable Agriculture & Food Innovation Platform will invest up to £90 million over the next five years in projects and initiatives across the agri-food sector, focusing on areas such as crop productivity, sustainable livestock production and the reduction of food chain waste and greenhouse gas emissions.
One of the projects that has won funding in this first tranche of grants, entitled ‘Automating weed mapping in arable fields for precision farming’ involves four companies, Masstock Arable UK Ltd, Knight Farm Machinery Ltd, Patchwork Technology Ltd, Syngenta Crop Protection UK, working with Reading University.
The aim is to develop a global positioning system-linked computer-controlled digital camera system that can be mounted on farm machinery, such as tractors, sprayers or combine harvesters, to map and geo-reference weeds such as black-grass, which occur in patches in arable crops. A machine vision system using digital cameras will be linked to image analysis software, to identify the weeds present and estimate their densities.
Benefits of the system include reducing the cost of weed control to the farmer, cutting herbicide use and the early detection of herbicide resistance.