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primary topic: Computer Vision for Automated Prawn Cultivation: Density and Growth Estimation

Computer Vision for Automated Prawn Cultivation: Density and Growth Estimation
type: AcademicArticle
type: Article
abstract: This study investigates the implementation of computer vision for automated prawn cultivation, focusing on density and growth estimation to contribute to aquaculture sustainability. Traditional methods of monitoring prawn density and growth involve manual measurements, which can be time-consuming, labor-intensive, and prone to human error. Addressing these challenges, the proposed system employs MobileNetV2 for prawn density estimation and achieves an accuracy of over 92. It successfully reduces the average cost and time required for farm monitoring at scale, making it a viable alternative to manual methodologies. © 2024 IEEE.
date: 2024
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ConferenceItemEPrint
is part of: repository

Institute of Electrical and Electronics Engineers Inc.
type: Organization

Chaiw Yee Teoh
type: Person
given name: Chaiw Yee
family name: Teoh

Yen Lin Chen
type: Person
given name: Yen Lin
family name: Chen

Boonyaik Yaik Ooi
type: Person
given name: Boonyaik Yaik
family name: Ooi

Ailin Ooi
type: Person
given name: Ailin
family name: Ooi

Teik Boon Tan
type: Person
given name: Teik Boon
family name: Tan

Xiao Wei Chong
type: Person
given name: Xiao Wei
family name: Chong

Tze Jun John Ling
type: Person
given name: Tze Jun John
family name: Ling

Wai Khuen Cheng
type: Person
given name: Wai Khuen
family name: Cheng

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primary topic: Computer Vision for Automated Prawn Cultivation: Density and Growth Estimation