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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| _version_ | 1854094476016877568 |
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| author | Lean, Jin Hao |
| author_facet | Lean, Jin Hao |
| author_sort | Lean, Jin Hao |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.7110 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my-utar-eprints.71102025-12-28T15:59:35Z Utilising computer vision techniques for automated density and growth estimation in precision aquaculture systems for prawn cultivation Lean, Jin Hao S Agriculture (General) T Technology (General) 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. 2025-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/7110/1/fyp_CS_2025_LJH.pdf Lean, Jin Hao (2025) Utilising computer vision techniques for automated density and growth estimation in precision aquaculture systems for prawn cultivation. Final Year Project, UTAR. http://eprints.utar.edu.my/7110/ |
| spellingShingle | S Agriculture (General) T Technology (General) Lean, Jin Hao Utilising computer vision techniques for automated density and growth estimation in precision aquaculture systems for prawn cultivation |
| title | Utilising computer vision techniques for automated density and growth estimation in precision aquaculture
systems for prawn cultivation |
| title_full | Utilising computer vision techniques for automated density and growth estimation in precision aquaculture
systems for prawn cultivation |
| title_fullStr | Utilising computer vision techniques for automated density and growth estimation in precision aquaculture
systems for prawn cultivation |
| title_full_unstemmed | Utilising computer vision techniques for automated density and growth estimation in precision aquaculture
systems for prawn cultivation |
| title_short | Utilising computer vision techniques for automated density and growth estimation in precision aquaculture
systems for prawn cultivation |
| title_sort | utilising computer vision techniques for automated density and growth estimation in precision aquaculture
systems for prawn cultivation |
| topic | S Agriculture (General) T Technology (General) |
| url | http://eprints.utar.edu.my/7110/1/fyp_CS_2025_LJH.pdf http://eprints.utar.edu.my/7110/ |
| url_provider | http://eprints.utar.edu.my |