Tilapia (oreochromis niloticus) fingerling counting system / Maricis I. de la Fuente [and 4 others].

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Dissertation note: Undergraduate Theses (BSECE) - Electronics Engineering. Camarines Sur Polytechnic Colleges, 2022. Abstract: This research project developed a counting system for Tilapia fingerlings by incorporating Otsu thresholding and Morphological Transformations algorithms in the system design. The different processes and technique procedure of fingerling counting for both the traditional and automatic counting methods were considered in the study and a number of image processing algorithms used in existing fish counters were explored. The existing systems usually require sophisticated hardware and computers that adds up to the cost of manufacturing. The present work used a Raspberry Pi 4 B Single Board Computer and Raspberry Pi Camera Module v1.3 to lessen the cost using computer vision and image processing algorithms calculated through a Confusion Matrix. The system prototype was designed for the three different sizes of tilapia fingerlings: Direkta, Potential and Oversized. However, it was only tested for the Direkta fingerlings. Moreover, the performance of the system was evaluated in terms of accuracy and speed of the device. The results implies that the automatic counting system developed by the project is a faster and more consistent method for counting tilapia fingerlings than the manual counting process, particularly for larger batch sizes.
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Unpublished Materials Unpublished Materials Main Library Undergraduate Thesis Section UTH BSECE D3703t 2022 (Browse shelf(Opens below)) Not for loan 006143UM

Undergraduate Theses (BSECE) - Electronics Engineering. Camarines Sur Polytechnic Colleges, 2022.

Includes bibliographical references.

This research project developed a counting system for Tilapia fingerlings by incorporating Otsu thresholding and Morphological Transformations algorithms in the system design. The different processes and technique procedure of fingerling counting for both the traditional and automatic counting methods were considered in the study and a number of image processing algorithms used in existing fish counters were explored. The existing systems usually require sophisticated hardware and computers that adds up to the cost of manufacturing. The present work used a Raspberry Pi 4 B Single Board Computer and Raspberry Pi Camera Module v1.3 to lessen the cost using computer vision and image processing algorithms calculated through a Confusion Matrix. The system prototype was designed for the three different sizes of tilapia fingerlings: Direkta, Potential and Oversized. However, it was only tested for the Direkta fingerlings. Moreover, the performance of the system was evaluated in terms of accuracy and speed of the device. The results implies that the automatic counting system developed by the project is a faster and more consistent method for counting tilapia fingerlings than the manual counting process, particularly for larger batch sizes.

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