Fish pellet measurement system for food industry

Ngo, Kok Wei (2025) Fish pellet measurement system for food industry. Final Year Project, UTAR.

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Abstract

This project aims to address the need for precise measurement of fish pellets in the industrial sector by leveraging technology. This project will involve several fields such as computer vision, embedded systems and deep learning. Before that, manpower is required to measure the fish pellets individually. By using this traditional method, the accuracy and efficiency are low. Therefore, a new method using technology will be introduced to solve this problem. The core technology used in this project is computer vision and deep learning. Initially, a camera will be set up to capture the fish pellet. Then, the image will be processed in a trained model to detect the fish pellet. Then, an algorithm will be used to determine the fish pellet's diameter based on the result of the detection. To improve the consistency, Raspberry Pi will be chosen as the CPU of this project. The camera will be interfaced to it, and the trained model will be imported into it. A user-friendly GUI will also be provided to display the output information of the system. Python will be selected as the programming language in this project due to its extensive library support, such as OpenCV for computer vision and TensorFlow for deep learning. As a result, the GUI should perform the fish pellet detection and diameter calculation, which has a bounded box and label of the diameter value on each fish pellet as the output. In conclusion, this project will provide a more efficient solution than the traditional method. Area of Study (Minimum 1 and Maximum 2): Internet of Things Vision Keywords (Minimum 5 and Maximum 5): Computer Vision, Deep Learning, IoT, Image Processing, Algorithm Design

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