Vision-based Shrimp Feed Type Classification using Fuzzy Logic

Shrimp farming is a major industry covering 23% of Philippine annual aquaculture production, which requires performing better management practices (BMPs) including growth monitoring and feed management. Traditionally, growth is monitored manually using analog weighing scale and caliper; but the manual measurement is a tedious task for large-scale farming. Feed management entails providing the most suitable feed type based on the shrimp's current growth stage; furthermore, it addresses issues of underfeeding and overfeeding. The limitations of manual method led to the implementation of computer vision applications for growth measurement. However, existing vision-based measurement studies are not yet applied for feed management. This paper presented a fuzzy-logic based shrimp feed type classification system utilizing Mamdani's methodology. The output classes are Starter, Grower, and Finisher based on the three inputs: pixel area, length, and weight. The system was developed using the FIS feature of the MATLAB Fuzzy Logic toolbox. The classification system was evaluated and resulted to 93.33% correct classification accuracy. Based on these results, it can be concluded that fuzzy logic can be utilized to determine the suitable shrimp feed type corresponding to the input features.