An Automated Fish-Feeding System Based on CNN and GRU Neural Networks

Abstract

AI plays a pivotal role in predicting plant growth in agricultural contexts and in creating optimized environments for cultivation. However, unlike agriculture, the application of AI in aquaculture is predominantly focused on diagnosing animal conditions and monitoring them for users. This paper introduces an Automated Fish-feeding System (AFS) based on Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs), aiming to establish an automated system akin to smart farming in the aquaculture sector. The AFS operates by precisely calculating feed rations through two main modules. The Fish Growth Measurement Module (FGMM) utilizes fish data to assess the current growth status of the fish and transmits this information to the Feed Ration Prediction Module (FRPM). The FRPM integrates sensor data from the fish farm, fish growth data, and current feed ration status as time-series data, calculating the increase or decrease rate of ration based on the present fish conditions. This paper automates feed distribution within fish farms through these two modules and verifies the efficiency of automated feed distribution. Simulation results indicate that the FGMM neural network model effectively identifies fish body length with a minor deviation of less than 0.1%, while the FRPM neural network model demonstrates proficiency in predicting ration using a GRU cell with a structured layout of 64 × 48.