Utilising computer vision techniques for automated density and growth estimation in precision aquaculture systems for prawn cultivation
Prawn farming, a vital sector of the global aquaculture industry, faces challenges with traditional monitoring methods that are labor-intensive, error-prone, and lack real-time capabilities, leading to inefficiencies in feeding and harvest planning, particularly for small- and medium-scale far...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Online Access: | http://eprints.utar.edu.my/7110/1/fyp_CS_2025_LJH.pdf http://eprints.utar.edu.my/7110/ |
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| Summary: | Prawn farming, a vital sector of the global aquaculture industry, faces challenges with
traditional monitoring methods that are labor-intensive, error-prone, and lack real-time
capabilities, leading to inefficiencies in feeding and harvest planning, particularly for
small- and medium-scale farmers. This project aims to address these issues by
developing a computer vision-based system for automated density and growth
estimation of Cherax quadricarinatus prawns, enhancing operational efficiency and
sustainability. Utilizing the lightweight YOLO11n neural network, a Raspberry Pi 5,
and a PiCamera (Night Vision), the system automates prawn monitoring, improves
accuracy through machine learning, and ensures affordability at $60-$80 per unit. A
Cron Job feature enables continuous data collection, building a farm-specific dataset to
overcome the lack of standardized prawn data. Deployed in a controlled pond
environment, the system captured 2000 images under varying conditions, achieving
real-time detection at 5 FPS, though initial tests revealed accuracy issues requiring
further data and fine-tuning. By mitigating challenges like environmental variability,
high costs, and technical complexity identified in prior studies, this solution offers a
scalable, user-friendly tool that empowers smaller farms to optimize resource use and
enhance productivity in precision aquaculture. |
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