Publication:
Feasibility Study of Fish Disease Detection using Computer Vision and Deep Convolutional Neural Network (DCNN) Algorithm

Simple item page
cris.author.scopus-author-id 57578158900
cris.author.scopus-author-id 57220805979
cris.author.scopus-author-id 57201059019
cris.author.scopus-author-id 57207458751
cris.virtual.department Universiti Malaysia Perlis
cris.virtualsource.department 147447f4-90a6-47ee-8445-2d068a624923
dc.contributor.author Muhammad Luqman Yasruddin
dc.contributor.author Muhammad Amir Hakim Ismail
dc.contributor.author Zulkifli Husin
dc.contributor.author Tan Wei Keong
dc.date.accessioned 2024-09-27T07:13:27Z
dc.date.available 2024-09-27T07:13:27Z
dc.date.issued 2022-01-01
dc.description.abstract Detection of diseased fish at an early stage is necessary to prevent the spread of the disease. However, detecting fish diseases still uses a manual process and requires a high level of expertise that can be prone to human error. The ability of automatic detection of these fish diseases is much needed to help and to prevent losses of economic in the aquaculture industry. Therefore, this paper aims to detect disease of fish using computer vision and deep convolutional neural network (DCNN) algorithm. One Thousand and Two Hundred fish samples images were selected is namely diseased fish and healthy fish, which is determined by expert of fish diseases according to the specific of characteristics of fish diseases. The fish images went through the DCNN classifier and successfully achieved a satisfying mean average precision (mAP) with 0.237. The result shows that the computer vision integrated with the DCNN algorithm can efficiently predict fish disease.
dc.identifier.doi 10.1109/CSPA55076.2022.9782020
dc.identifier.isbn [9781665485296]
dc.identifier.scopus 2-s2.0-85132713103
dc.identifier.uri https://hdl.handle.net/20.500.14170/4694
dc.relation.funding Universiti Malaysia Perlis
dc.relation.grantno undefined
dc.relation.ispartof 2022 IEEE 18th International Colloquium on Signal Processing and Applications, CSPA 2022 - Proceeding
dc.relation.ispartofseries 2022 IEEE 18th International Colloquium on Signal Processing and Applications, CSPA 2022 - Proceeding
dc.subject computer vision | deep convolutional neural network | fish disease detection
dc.title Feasibility Study of Fish Disease Detection using Computer Vision and Deep Convolutional Neural Network (DCNN) Algorithm
dc.type Conference Proceeding
dspace.entity.type Publication
oaire.citation.endPage 276
oaire.citation.startPage 272
oairecerif.affiliation.orgunit Centre of Excellence Faculty of Electronic Engineering Technology Universiti Malaysia
oairecerif.affiliation.orgunit Centre of Excellence Faculty of Electronic Engineering Technology Universiti Malaysia
oairecerif.affiliation.orgunit Centre of Excellence Faculty of Electronic Engineering Technology Universiti Malaysia
oairecerif.affiliation.orgunit Centre of Excellence Faculty of Electronic Engineering Technology Universiti Malaysia
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.scopus-author-id 57578158900
person.identifier.scopus-author-id 57220805979
person.identifier.scopus-author-id 57201059019
person.identifier.scopus-author-id 57207458751
Files
Collections
Publications 2022
Load more