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Optimizing high-density aquaculture rotifer Detection using deep learning algorithm

The dynamics of marine aquaculture depend heavily on zooplankton, which is the basis of the marine food chain. Zooplankton like Rotifer brachionus plicatillis, which are rich in nutrients, small size and rapid reproductive rate are necessary for fish in the larval stage. Rotifer must therefore be su...

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Main Authors: Alixson Polumpung, Kit Guan Lim, Min Keng Tan, Sitti Raehanah Muhamad Shaleh, Renee Ka Yin Chin, Kenneth Teo Tze Kin
Format: Proceedings
Language:English
English
Published: IEEE Xplore 2022
Subjects:
QA75-76.95 Calculating machines
SH20.3-191 Aquaculture
Online Access:https://eprints.ums.edu.my/id/eprint/41729/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/41729/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/41729/
https://ieeexplore.ieee.org/document/9936794
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spelling my.ums.eprints.417292024-11-04T01:40:21Z https://eprints.ums.edu.my/id/eprint/41729/ Optimizing high-density aquaculture rotifer Detection using deep learning algorithm Alixson Polumpung Kit Guan Lim Min Keng Tan Sitti Raehanah Muhamad Shaleh Renee Ka Yin Chin Kenneth Teo Tze Kin QA75-76.95 Calculating machines SH20.3-191 Aquaculture The dynamics of marine aquaculture depend heavily on zooplankton, which is the basis of the marine food chain. Zooplankton like Rotifer brachionus plicatillis, which are rich in nutrients, small size and rapid reproductive rate are necessary for fish in the larval stage. Rotifer must therefore be supplied to larvae culture in the correct quantity, which can be determined by counting it. In addition, it is necessary to estimate the rotifer population to ensure that, aside from care, it can support the demands of all larvae batches. Currently, the traditional method of counting small-sized rotifers still involves counting it manually. One easy potential way to count rotifer is by using binary image segmentation provided that the sample is clear from debris. In this paper, we present the method and performance to detect rotifer Brachionus plicatilis in 1ml sample automatically using deep learning algorithm YOLOv3. Detected rotifer will be counted for estimating the amount of rotifer for feeding or the density population in a rotifer culture. The method of this project consists of following steps. First, dataset acquisition from digital microscope and manual labelling annotation divided by 60, 20 and 20 percent for training, validation and testing consecutively. Second, is to develop the deep learning algorithm based on YOLOv3. Third step is to training and evaluate the model using loss function. Finally, the model is tested with average precision of 85.1 percent with average of 1.4645s inference detection speed IEEE Xplore 2022 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/41729/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/41729/2/FULL%20TEXT.pdf Alixson Polumpung and Kit Guan Lim and Min Keng Tan and Sitti Raehanah Muhamad Shaleh and Renee Ka Yin Chin and Kenneth Teo Tze Kin (2022) Optimizing high-density aquaculture rotifer Detection using deep learning algorithm. https://ieeexplore.ieee.org/document/9936794
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic QA75-76.95 Calculating machines
SH20.3-191 Aquaculture
spellingShingle QA75-76.95 Calculating machines
SH20.3-191 Aquaculture
Alixson Polumpung
Kit Guan Lim
Min Keng Tan
Sitti Raehanah Muhamad Shaleh
Renee Ka Yin Chin
Kenneth Teo Tze Kin
Optimizing high-density aquaculture rotifer Detection using deep learning algorithm
description The dynamics of marine aquaculture depend heavily on zooplankton, which is the basis of the marine food chain. Zooplankton like Rotifer brachionus plicatillis, which are rich in nutrients, small size and rapid reproductive rate are necessary for fish in the larval stage. Rotifer must therefore be supplied to larvae culture in the correct quantity, which can be determined by counting it. In addition, it is necessary to estimate the rotifer population to ensure that, aside from care, it can support the demands of all larvae batches. Currently, the traditional method of counting small-sized rotifers still involves counting it manually. One easy potential way to count rotifer is by using binary image segmentation provided that the sample is clear from debris. In this paper, we present the method and performance to detect rotifer Brachionus plicatilis in 1ml sample automatically using deep learning algorithm YOLOv3. Detected rotifer will be counted for estimating the amount of rotifer for feeding or the density population in a rotifer culture. The method of this project consists of following steps. First, dataset acquisition from digital microscope and manual labelling annotation divided by 60, 20 and 20 percent for training, validation and testing consecutively. Second, is to develop the deep learning algorithm based on YOLOv3. Third step is to training and evaluate the model using loss function. Finally, the model is tested with average precision of 85.1 percent with average of 1.4645s inference detection speed
format Proceedings
author Alixson Polumpung
Kit Guan Lim
Min Keng Tan
Sitti Raehanah Muhamad Shaleh
Renee Ka Yin Chin
Kenneth Teo Tze Kin
author_facet Alixson Polumpung
Kit Guan Lim
Min Keng Tan
Sitti Raehanah Muhamad Shaleh
Renee Ka Yin Chin
Kenneth Teo Tze Kin
author_sort Alixson Polumpung
title Optimizing high-density aquaculture rotifer Detection using deep learning algorithm
title_short Optimizing high-density aquaculture rotifer Detection using deep learning algorithm
title_full Optimizing high-density aquaculture rotifer Detection using deep learning algorithm
title_fullStr Optimizing high-density aquaculture rotifer Detection using deep learning algorithm
title_full_unstemmed Optimizing high-density aquaculture rotifer Detection using deep learning algorithm
title_sort optimizing high-density aquaculture rotifer detection using deep learning algorithm
publisher IEEE Xplore
publishDate 2022
url https://eprints.ums.edu.my/id/eprint/41729/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/41729/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/41729/
https://ieeexplore.ieee.org/document/9936794
_version_ 1814935161371885568
score 13.223943

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