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A Self-learning Approach for Beggiatoa Coverage Estimation in Aquaculture

Citation

Chen, Y and Zhou, Y and Tran, S and Park, M and Hadley, S and Lacharite, M and Bai, Q, A Self-learning Approach for Beggiatoa Coverage Estimation in Aquaculture, Lecture Notes in Artificial Intelligence 13151, 02-04 February 2022, Sydney, pp. 405-416. ISSN 0302-9743 (2022) [Refereed Conference Paper]


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DOI: doi:10.1007/978-3-030-97546-3_33

Abstract

Beggiatoa is a bacterium that is associated with anoxic conditions beneath salmon aquaculture pens. Assessing the percentage coverage on the seafloor from images taken beneath a site is often undertaken as part of the environmental monitoring process. Images are assessed manually by observers with experience in identifying Beggiatoa. This is a time-consuming process and results can vary significantly between observers. Manually labelling images in order to apply visual learning techniques is also time-consuming and expensive as deep learning relies on very large data sets for training. Image segmentation techniques can automatically annotate images to release human resources and improve assessment efficiency. This paper introduces a combination method using Otsu thresholding and Fully Convolutional Networks (FCN). The self-learning method can be used to estimate coverage and generate training and testing data set for deep learning algorithms. Results showed that this combination of methods had better performance than individual methods.

Item Details

Item Type:Refereed Conference Paper
Keywords:artificial intelligence, aquaculture, Beggiatoa, self-learning
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Artificial life and complex adaptive systems
Objective Division:Environmental Management
Objective Group:Coastal and estuarine systems and management
Objective Field:Assessment and management of coastal and estuarine ecosystems
UTAS Author:Chen, Y (Miss Yanyu Chen)
UTAS Author:Zhou, Y (Miss Yunjue Zhou)
UTAS Author:Tran, S (Dr Son Tran)
UTAS Author:Park, M (Dr Mira Park)
UTAS Author:Hadley, S (Mr Scott Hadley)
UTAS Author:Lacharite, M (Dr Myriam Lacharite)
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:154916
Year Published:2022
Deposited By:Sustainable Marine Research Collaboration
Deposited On:2023-01-18
Last Modified:2023-01-18
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