Optimizing Fish Feeding with FFAUNet Segmentation and Adaptive Fuzzy Inference System

Author:

Huang Yo-Ping1234ORCID,Vadloori Spandana1ORCID

Affiliation:

1. Department of Electrical Engineering, National Penghu University of Science and Technology, Penghu 88046, Taiwan

2. Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan

3. Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 23741, Taiwan

4. Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan

Abstract

Efficient and optimized fish-feeding practices are crucial for enhancing productivity and sustainability in aquaculture. While many studies have focused on classifying fish-feeding intensity, there is a lack of research on optimizing feeding, necessitating a precise and automated model. This study fills this gap with a hybrid solution for precision aquaculture feeding management involving segmentation and optimization phases. In the segmentation phase, we used the novel feature fusion attention U-Net (FFAUNet) to accurately segment fish-feeding intensity areas. The FFAUNet achieved impressive metrics: a mean intersection over union (mIoU) of 89.39%, a mean precision of 95.07%, a mean recall of 95.08%, a mean pixel accuracy of 95.12%, and an overall accuracy of 95.61%. In the optimization phase, we employed an adaptive neuro-fuzzy inference system (ANFIS) with a particle swarm optimizer (PSO) to optimize feeding. Extracting feeding intensity percentages from the segmented output, the ANFIS with PSO achieved an accuracy of 98.57%, a sensitivity of 99.41%, and a specificity of 99.53%. This model offers fish farmers a robust, automated tool for precise feeding management, reducing feed wastage and improving overall productivity and sustainability in aquaculture.

Funder

National Science and Technology Council, Taiwan

Publisher

MDPI AG

Reference51 articles.

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