Automated Fish Sorting and Harvesting System for Nile Tilapia (Oreochromis Niloticus) Using Computer Vision / John Michael S. Garcia and Rowilyn B. Cortez.
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TextPublication details: Rosario, Cavite : Cavite State University-CCAT Campus, 2019Description: x, 35 leaves : illustrations ; 28 cmSubject(s): LOC classification: - UM TA 1634 G37 2019
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Design Project (BSCpE)--Cavite State University-CCAT Campus, 2019.
Includes bibliographical references and appendices.
GARCIA, JOHN MICHAEL S., CORTEZ, ROWILYN B. Automated Fish Sorting and Harvesting System for Nile Tilapia (Oreochromis Niloticus) Using Computer Vision. Design Project. Department of Engineering. Cavite State University-College of Arts and Trades Campus, Rosario, Cavite. January 2019, Adviser: Engr. John Michael Dharma; Technical Critic: Engr. Gee Jay C. Bartolome.
The study was conducted on October 2018 to develop and test the automated fish sorting and harvesting system for Nile tilapia. The study aimed to: 1) Design the hardware and software interface of the automated fish sorting and harvesting system for Nile talapia, 2) Evaluate the performance of the automated fish sorting and harvesting system in determining the following parameters: 2.1) Length, 2.2) Girth, 2.3) Mass, 2.4) Estimate the number of fishes in the fish tank, 3) Determine the accuracy of the measurements of the fish characteristics by comparison to manual measurements; and 4) Conduct cost and return analysis.
The automated fish sorting and harvesting system is made up of the following components: Raspberry Pi 3 Model B; Raspberry Pi NoIR Camera V2; L298N Motor Driver Board; 24V DC Geared Motor; Clear Acrylic Sheets with varying sizes and thickness: 24V 8.3A Switching Power Supply and Official Raspberry Pi 2.5A Power Supply. These components produce an automated fish sorting and harvesting system for Nile tilapia that can sort fishes into three different sizes. The system has Pi camera that has an 8-megapixel resolution and has a framerate of up to 30 frames per second. The system can measure fishes accurately and can also count the number of fishes that it is processing. The farmers can gather essential data and can sort and harvest fish remotely and can be done without much human interaction and thus lessen cost for human labor and save time.
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