Aquaponics is the merging of aquaculture with hydroponics in which nutrients supplied by aquatic life are used by plants grown in a soilless culture under regulated conditions. Potential advantages of aquaponics include reduced resource consumption, fewer environmental impacts, and improved sustainability compared to conventional aquaculture. In this paper, we have investigated how machine learning techniques can be used to predict the optimal environmental factors associated with the aquaculture subsystem, in order to improve overall fish production and sustainability. We used linear regression model to analyze the aquaponics dataset having various parameters (i.e., temperature, turbidity, dissolved oxygen, water pH, ammonia, Nitrate, and population) associated with aquatic life in the aquarium. Our initial findings suggest that the use of machine learning in predicting the optimal environmental factors for aquatic life can lead to significant improvements in fish production. Future work in this area could involve using deep learning techniques as a means of addressing the problem of multi-class classification of fish aquaculture environment.
Keywords: Aquaponics, Deep Learning, Fish Production