Design and Development of a Bangus Sorting and Quantifying Machine Using ArUco Marker-Based Image Processing

Explores the creation of a machine that effectively sorts and quantifies fish based on precise sizing criteria, contributing to increased customer satisfaction and market reliability. The machine operates by using appropriate camera lighting and the Raspberry Pi platform with ArUco markers for fish sizing detection and conveyor integration. This setup ensures accurate fish recognition and seamless transfer onto a running conveyor belt for sorting. The integration of camera technology enhances the precision of sizing, to integrate of new way of production using image processing. The core functionality of the Fish Sorting and Quantifying Machine lies in its ability to sort fish according to predefined size categories and deposit them into respective containers, streamlining the harvesting processing to store data of the fish measurement to cloud. The results represent a sustainable and eco-friendly solution for fish farming, having 88.41% accuracy using a confusion matrix utilizing advanced technology to provide a reliable, portable, and cost-effective means of sorting and quantifying fish. This innovation enhances fish harvesting processes and promotes environmental consciousness by minimizing reliance on manual labor. This research will delve into the detailed design and development process of the Bangus Sorting and Quantifying Machine, highlighting its technical specifications, operational efficiency, and economic impact on the local aquaculture industry. Through this research and innovation anticipate, solving a critical problem and setting a new standard for the region's efficient and sustainable fish farming practices. Further research and technological advancements in this field hold great potential for improving bangus sorting and quantifying machines and promoting sustainable fish farming practices.