Operation of Unmanned Automated Aquaculture Feeding System

Unmanned automatic feeding systems are being actively distributed to automate land-based aquaculture farms. Currently, feed supply in aquaculture is primarily based on a fixed period and amount, depending on the predetermined feed quantity set by the operator, which does not consider the dynamic feed supply needs of fish. This can lead to under- or oversupply of feed, affecting the fish growth rate and water quality in aquaculture. To address this, a method is proposed in this paper to identify fish hunger levels using machine vision and predict the dissolved oxygen concentration in real time to prevent mass fish mortality due to water quality deterioration, even with additional feed supply. The method aims to verify its performance. Firstly, based on the observation that aquaculture farm operators estimate fish hunger levels by feeding time, a method is proposed to estimate hunger levels using machine vision and feeding time. Next, a combination of time series data decomposition model and pre-trained deep learning model is used to predict the dissolved oxygen concentration in real time.