Shrimp aquaculture plays a vital role in global seafood production, contributing substantially to food security, economic growth, and export revenue. Feed typically accounts for 40–60% of total production costs, making efficient feed management crucial for improving farm profitability and the sustainability of culture operations. Acoustic-based feeding strategies offer a promising solution by enabling demand-driven feed control through the detection of shrimp feeding sounds. However, reliable recognition in commercial ponds remains difficult due to strong background noise from aerators, pumps, diffusers, and rainfall, which overlaps with the frequency band of the feeding signals. In addition, the dependence on specialized software and high-performance computing resources hinders large-scale adoption. This study proposes a novel shrimp feeding sound recognition approach that converts acoustic signals into spectrogram images and employs a Faster R-CNN–based framework to regulate feed delivery in real time according to shrimp demand. A wavelet-based filtering method is introduced to effectively suppress ambient noise under practical farming conditions. Moreover, the developed open-source Python-based software enhances the feasibility of deploying intelligent acoustic-based feeding systems in commercial shrimp aquaculture. Experimental results demonstrate that the proposed system improves feed utilization efficiency and growth performance compared with traditional feeding practices.
Hung et al. (2026) studied this question.