The application of artificial intelligence to benthic species identification

(HOWELL_P20ARIES)

The application of artificial intelligence to benthic species identification

(HOWELL_P20ARIES)

Project Description

Supervisors

Dr Kerry Howell, School of Biological and Marine Sciences, University of Plymouth

Dr Phil Culverhouse, School of Engineering, Computing and Mathematics, University of Plymouth

Dr Jill Schwarz, School of Biological and Marine Sciences, University of Plymouth

 

Background

Marine benthic ecosystems are chronically under-sampled particularly in environments >50m. Yet a rising level of anthropogenic threats makes data collection ever more urgent. Currently, modern underwater sampling tools, particularly Autonomous Underwater Vehicles (AUV) and Remotely Operated Vehicles (ROV), are able to collect vast image datasets, but cannot bypass the bottleneck formed by manual image annotation. Computer Vision (CV) can be a faster, more consistent, cost effective and a sharable alternative to manual annotation.

The application of CV to benthic ecology is in its infancy. Recent research has shown some promising results, however there is a need for further development of both methods and tools available in order to bring CV into the tool box used in benthic biodiversity and ecological studies.

Methodology

This studentship will focus on the development and testing of an effective CV based image processing pipeline. It will test the application of both existing CV tools (for example using Matlab, Google’s Tensor Flow or R based algorithms) as well as novel methods including use of underwater hyperspectral image data, and hybrid CV models.

Training

The student will have a unique opportunity to expand their outlook into a highly multi-disciplinary domain. They will interact with ecologists, computer scientists, engineers, ocean scientists, and photographers developing a wide network beyond the supervisory team. Depending on their background the student may receive training in ecology and taxonomy, computer vision, machine learning, marine optics, Matlab, R and Python programming. A degree in either an ecological field, computer science field, or other highly numerate field e.g. MTH, engineering etc is required.

Person Specification

We recognise that candidates are unlikely to have both ecological and programming skills. Thus we are looking for someone with a strong mathematical background and a demonstrable capacity to learn new skills and adapt their knowledge to new situations. Skills in use of statistical and / or computational models (for example one or more of the following – GLMS, GAMS, multivariate statistics, machine learning, convolutional neural networks) are essential.

References

  • 1. Culverhouse PF, Williams R, Reguera B, Herry V, González-Gil S (2003) Do experts make mistakes? A comparison of human and machine identification of dinoflagellates. Marine Ecology Progress Series 247:17-25
  • 2. Howell KL, Davies JS, Allcock AL, Braga-Henriques A, Buhl-Mortensen P et al (2019) A framework for the development of a global standardised marine taxon reference image database (SMarTaR-ID) to support image-based analyses. BioRXiv and in review at PlosOne. https://doi.org/10.1101/670786
  • 3. MacLeod N, Benfield M, Culverhouse P (2010) Time to automate identification. Nature 467:154-155
  • 4. Piechaud N, Culverhouse PF, Hunt C, Howell KL. (2019) Automated Identification of benthic epifauna from images using computer vision. Marine Ecology Progress Series. 615, 15-30.
  • 5. Schwarz JN, (2001). The use of high spectral resolution in-situ optical data for monitoring case II (coastal) water quality. PhD University of Southampton.

Key Information

  • This project has been shortlisted for funding by the ARIES NERC Doctoral Training Partnership, and will involve attendance at mandatory training events throughout the course of the PhD.
  • Successful candidates who meet UKRI’s eligibility criteria will be awarded a NERC studentship - UK and EU nationals who have been resident in the UK for 3 years are eligible for a full award.
  • Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses in the subject area (see https://www.aries-dtp.ac.uk/supervisors/additional-funding/).
  • This studentship will start on 1st October 2020, and the closing date for applications is 12:00 on 7th January 2020.
  • Shortlisted applicants will be interviewed on 18/19 February 2020.
  • For further information, please contact the supervisor.
  • Please note that the joint NERC-ESRC ARIES-SeNSS studentship projects have different deadlines and funding arrangements. For full details please visit https://senss-dtp.ac.uk/aries-senss-joint-studentship, or contact SeNSS.dtp@uea.ac.uk.

Studentship Open for Applications

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