<?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/7812794A-9D70-4DB5-9BE0-CD0E65DB7144" ns1:id="7812794A-9D70-4DB5-9BE0-CD0E65DB7144"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/D08B7221-EE82-4220-84CC-B4BF8B11ECF1" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/3633048E-16EA-4920-B619-C039ED73CE02" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/7FD5F131-B3EF-479A-B155-222556B3F7DE" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/3633048E-16EA-4920-B619-C039ED73CE02" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2018-01-31T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/BC6DC487-756C-4B8B-8A32-BDDA9C35D320" ns1:rel="FUND" ns1:start="2016-08-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">132338</ns2:identifier></ns2:identifiers><ns2:title>3D Vision-based Crop-Weed Discrimination for Automated Weeding Operations</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>BEIS-Funded Programmes</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>The current crop production systems have been reliant on the wide-scale application of herbicides to

control weeds. However, this approach is not sustainable due to unprecedented regulatory and

environmental pressures which place new emphasis on the development of novel techniques to kill

weeds.This project will investigate the technical foundations for the next generation of robotic weeding

machinery, enabling selective and accurate treatment of specific weeds. The proposed technology is a

novel combination of low-cost 3D sensing and learning software together with a suitable weed

destruction technique. The proposed developments will lead to more efficient cultural weeding

equipment resulting in better management of weeds and reduced input use, bringing several benefits to

food producers, sellers and society.</ns2:abstractText></ns2:project>