<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/91F01156-0A5F-42C1-9466-8B7EB3704AC9" ns1:id="91F01156-0A5F-42C1-9466-8B7EB3704AC9"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/1B391CAA-A45E-433B-BA2E-B091BA4E12E0" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/FAABF2EB-AEB2-4A67-A79E-39B8F8121932" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/FAABF2EB-AEB2-4A67-A79E-39B8F8121932" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/34889DCD-6DC0-46E8-B21F-A6133021E411" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/EE51FD90-3802-472B-98BB-5C0046A6B598" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2023-03-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/F6F0C835-B9D1-4A8F-82C2-F8823884628B" ns1:rel="FUND" ns1:start="2021-09-30T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10002902</ns2:identifier></ns2:identifiers><ns2:title>Integrating Visual and Context Information into a Mobile Intelligence Solution for Sustainable Management of Wheat Pests and Soil Health</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>ISCF</ns2:leadFunder><ns2:abstractText>Sustainable management of UK wheat pests and maintenance of soil health have become a high-priority agricultural issue in the UK. This project will investigate the technical feasibility of integrating visual and contextual information with advanced data fusion techniques into a mobile pest management solution that offers: rapid detection and quantification of wheat pest by mobile devices; efficient forecasting of accepted pest thresholds for sustainable management; estimation of the corresponding efficacy of a pesticide for pest control. The project will be led by University of Sheffield, and build on existing technologies, data resources and platforms from previous projects within the consortium.</ns2:abstractText></ns2:project>