Integrating IoT and Deep Learning for Smart Aquaculture Management in Freshwater Aquariums

Aquaculture farming practices are facing common challenges related to water quality monitoring and management. Aquatic production systems cater to small farmers, hobbyists, and commercial enterprises alike. This project aims to address common challenges encountered in conventional aquaculture farming practices. Water is an essential component for all living beings, and monitoring water quality and its parameters in freshwater aquariums is a must to get safe water with up-optimal water management and disproportionate use of fertilizers during fish farming. The proposed method utilizes technological advancements, integrating sensors, cloud connectivity and loT platforms. These components play a crucial role in monitoring key parameters essential for successful aquaculture, such as pH levels, temperature, water levels, and dissolved oxygen requirements. The proposed system aims to enhance fish growth rates and production by optimizing parametric conditions such as temperature, pH levels, dissolved oxygen, water level, ammonia, nitrite and nitrates. Finally, the feeding schedule and feed distribution are optimized using Boosted Binary Harris Hawks Optimization (BB-HHO). This project involves establishing seamless communication between the controller and mobile phone or web applications. Through cloud platforms, farmers can remotely access real-time vital updates about their aquatic farms, even when they are physically distant.