TY  - CONF
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205767548&doi=10.1109%2FICCE-Taiwan62264.2024.10674333&partnerID=40&md5=c30c101c24c08fb31f773f79d1870c54
Y1  - 2024///
TI  - Computer Vision for Automated Prawn Cultivation: Density and Growth Estimation
N1  - Cited by: 2
PB  - Institute of Electrical and Electronics Engineers Inc.
EP  -  238
ID  - scholars20423
AV  - none
SN  - 9798350386844
SP  - 237 
A1  - Cheng, Wai Khuen
A1  - Ooi, Boonyaik Yaik
A1  - Tan, Teik Boon
A1  - Chong, Xiao Wei
A1  - Ling, Tze Jun John
A1  - Teoh, Chaiw Yee
A1  - Ooi, Ailin
A1  - Chen, Yen Lin
N2  - 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.
KW  - Average cost; Density estimation; Growth estimation; Human errors; Labour-intensive; Manual measurements; Prawn cultivation; Precision aquaculture; Aquaculture
ER  -