<?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="2026-06-22T07:57:45Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/3029D863-C41C-4CA3-A251-A06F48BECE6F" ns1:id="3029D863-C41C-4CA3-A251-A06F48BECE6F"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/007EB8AD-22B3-4074-85CE-3E2D14F4459C" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/14B9575D-EE36-4E7E-B9BF-CC592545B085" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/14B9575D-EE36-4E7E-B9BF-CC592545B085" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/182E0F95-3A95-4546-AED4-63101C7452EE" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2022-12-31T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/36D0DD67-5AE2-4253-BB5C-04F5180417D0" ns1:rel="FUND" ns1:start="2021-06-30T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10004706</ns2:identifier></ns2:identifiers><ns2:title>Autonomous Acoustic Ground Discrimination System for Seagrass Characterisation</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>HydroSurv Unmanned Survey (UK) Ltd, (_HydroSurv_), an early-stage impact-focused designer, builder and operator of Uncrewed Surface Vehicles (USVs) is collaborating with the University of Plymouth to develop a new tightly-integrated robotic survey platform that features a new Acoustic Ground Discrimination System (AGDS) to put a transformative non-invasive, low-impact and economic monitoring solution for seagrass meadows in the hands of conservation bodies.

Building upon the existing research performed by University of Plymouth, the project will develop an end-to-end solution from the sensor and embedded vehicle components to a cloud-based data-hosting solution optimised for seagrass mapping, providing an economic change comparison modelling between resurveys. The processing solution will involve developing and training new machine-learning algorithms to classify submerged aquatic vegetation.

The project provides more than 43 days of on-water validation and testing, including technology demonstrations for review by key stakeholders Natural England (solution tester), South West Lakes Trust and the National Oceanography Centre.

The solution is developed to provide a new technology and a new capability to respond to future needs to protect and restore our seagrass habitats around the globe, driving benefits such as blue carbon sequestration, protection of marine biodiversity and creating the conditions for security of fisheries and ocean food sources.</ns2:abstractText></ns2:project>