A Reinforcement Learning based Hybrid GR-DQN Model for Predicting Ichthyophthiriosis Disease in Aquaculture Through Water Quality Analysis

Aquaculture is a fast-growing industry that provides nutritious food to a growing population and generates substantial revenue for countries. The high water quality is required to be maintained for aquatic animal’s survival and health; otherwise, it may cause many diseases like Furunculosis, Bacterial gill disease, and others. Traditionally available methods for water quality analysis are typically difficult to perform due to being time-consuming and lacking accuracy. In this study, a new approach has been developed using an optimal deep reinforcement learning technique, Hybrid Gated Recurrent Unit (GRU) network with Deep Q-Network (DQN), to analyze the state of the water quality of aquaculture by predicting Ichthyophthiriosis (white spot diseases) in an aquaculture environment. The GRU deep learning model with DQN helps in improving the prediction by approximating Q-values and produces a loss function to guide the learning process; rewards are provided due to correct predictions, thereby disease detection corrected accuracy was enhanced. The proposed hybrid GR-DQN model was implemented on the “Pondsdata” dataset and compared the results with the existing model M-DQN. The Hybrid GR-DQN achieved 94.69% accuracy in comparison to the existing model M-DQN’s 84.16% accuracy on the same dataset.