relation: https://khub.utp.edu.my/scholars/20423/
title: Computer Vision for Automated Prawn Cultivation: Density and Growth Estimation
creator: Cheng, Wai Khuen
creator: Ooi, Boonyaik Yaik
creator: Tan, Teik Boon
creator: Chong, Xiao Wei
creator: Ling, Tze Jun John
creator: Teoh, Chaiw Yee
creator: Ooi, Ailin
creator: Chen, Yen Lin
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Cheng, Wai Khuen and Ooi, Boonyaik Yaik and Tan, Teik Boon and Chong, Xiao Wei and Ling, Tze Jun John and Teoh, Chaiw Yee and Ooi, Ailin and Chen, Yen Lin  (2024) Computer Vision for Automated Prawn Cultivation: Density and Growth Estimation.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205767548&doi=10.1109%2FICCE-Taiwan62264.2024.10674333&partnerID=40&md5=c30c101c24c08fb31f773f79d1870c54
relation: 10.1109/ICCE-Taiwan62264.2024.10674333
identifier: 10.1109/ICCE-Taiwan62264.2024.10674333