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AI-image processing and image recognition for intelligent prawn farming

This project aims to explore the untapped potential of Convolutional Neural Networks (CNNs) within the realm of Prawn Farming. In the domain of deep learning, CNNs are recognized as a set of neural networks primarily designed for processing spatial data, such as images and videos. This research proj...

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Bibliographic Details
Main Author: Khor, Jia Cheng
Format: Final Year Project / Dissertation / Thesis
Published: 2023
Subjects:
S Agriculture (General)
SH Aquaculture. Fisheries. Angling
T Technology (General)
TD Environmental technology. Sanitary engineering
Online Access:http://eprints.utar.edu.my/6035/1/fyp_CS_2023_KJC.pdf
http://eprints.utar.edu.my/6035/
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id my-utar-eprints.6035
record_format eprints
spelling my-utar-eprints.60352024-01-02T14:49:55Z AI-image processing and image recognition for intelligent prawn farming Khor, Jia Cheng S Agriculture (General) SH Aquaculture. Fisheries. Angling T Technology (General) TD Environmental technology. Sanitary engineering This project aims to explore the untapped potential of Convolutional Neural Networks (CNNs) within the realm of Prawn Farming. In the domain of deep learning, CNNs are recognized as a set of neural networks primarily designed for processing spatial data, such as images and videos. This research project is dedicated to applying various types of CNNs to detect the growth stages of the Giant Freshwater Prawn and find out the most suitable. Simultaneously, it endeavours to uncover the broader contributions that CNN models can make to the Prawn Farming industry and the broader aquaculture ecosystem. The CNN models employed in this research include You Only Look Once (YOLOv7), Faster-RCNN ResNet101, SSD ResNet101, Centernet Hourglass 104, SSD MobileNet V1, and Faster-RCNN ResNet50 v1. The research process involves an extensive literature review and in-depth research to gain a comprehensive understanding of the application of Machine Learning in Prawn Farming and the broader aquaculture system. Through this review, it becomes evident that, in contrast to traditional Artificial Intelligence (AI) models, CNNs have emerged as a prominent trend in image processing and recognition. These insights underscore the rationale for conducting this project. Furthermore, data acquisition stands as a pivotal aspect of this research project due to the unavailability of the required image data from existing sources. Consequently, a dedicated dataset has been meticulously curated and made accessible on Kaggle for use by fellow researchers in the future. The outcomes of this research project offer valuable insights into the selection of suitable CNN models for Giant Freshwater Prawn growth stage classification, believing will be able to provide guidance for future new entrants into this field. 2023-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6035/1/fyp_CS_2023_KJC.pdf Khor, Jia Cheng (2023) AI-image processing and image recognition for intelligent prawn farming. Final Year Project, UTAR. http://eprints.utar.edu.my/6035/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic S Agriculture (General)
SH Aquaculture. Fisheries. Angling
T Technology (General)
TD Environmental technology. Sanitary engineering
spellingShingle S Agriculture (General)
SH Aquaculture. Fisheries. Angling
T Technology (General)
TD Environmental technology. Sanitary engineering
Khor, Jia Cheng
AI-image processing and image recognition for intelligent prawn farming
description This project aims to explore the untapped potential of Convolutional Neural Networks (CNNs) within the realm of Prawn Farming. In the domain of deep learning, CNNs are recognized as a set of neural networks primarily designed for processing spatial data, such as images and videos. This research project is dedicated to applying various types of CNNs to detect the growth stages of the Giant Freshwater Prawn and find out the most suitable. Simultaneously, it endeavours to uncover the broader contributions that CNN models can make to the Prawn Farming industry and the broader aquaculture ecosystem. The CNN models employed in this research include You Only Look Once (YOLOv7), Faster-RCNN ResNet101, SSD ResNet101, Centernet Hourglass 104, SSD MobileNet V1, and Faster-RCNN ResNet50 v1. The research process involves an extensive literature review and in-depth research to gain a comprehensive understanding of the application of Machine Learning in Prawn Farming and the broader aquaculture system. Through this review, it becomes evident that, in contrast to traditional Artificial Intelligence (AI) models, CNNs have emerged as a prominent trend in image processing and recognition. These insights underscore the rationale for conducting this project. Furthermore, data acquisition stands as a pivotal aspect of this research project due to the unavailability of the required image data from existing sources. Consequently, a dedicated dataset has been meticulously curated and made accessible on Kaggle for use by fellow researchers in the future. The outcomes of this research project offer valuable insights into the selection of suitable CNN models for Giant Freshwater Prawn growth stage classification, believing will be able to provide guidance for future new entrants into this field.
format Final Year Project / Dissertation / Thesis
author Khor, Jia Cheng
author_facet Khor, Jia Cheng
author_sort Khor, Jia Cheng
title AI-image processing and image recognition for intelligent prawn farming
title_short AI-image processing and image recognition for intelligent prawn farming
title_full AI-image processing and image recognition for intelligent prawn farming
title_fullStr AI-image processing and image recognition for intelligent prawn farming
title_full_unstemmed AI-image processing and image recognition for intelligent prawn farming
title_sort ai-image processing and image recognition for intelligent prawn farming
publishDate 2023
url http://eprints.utar.edu.my/6035/1/fyp_CS_2023_KJC.pdf
http://eprints.utar.edu.my/6035/
_version_ 1787140951710367744
score 13.211869

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