IoT-Based Environmental Control System for Fish Farms with Sensor
  Integration and Machine Learning Decision Support
D. Dhinakaran S. Gopalakrishnan M. D. Manigandan T. P. Anish
In response to the burgeoning global demand for seafood and the challenges of
managing fish farms, we introduce an innovative IoT based environmental control
system that integrates sensor technology and advanced machine learning decision
support. Deploying a network of wireless sensors within the fish farm, we
continuously collect real-time data on crucial environmental parameters,
including water temperature, pH levels, humidity, and fish behavior. This data
undergoes meticulous preprocessing to ensure its reliability, including
imputation, outlier detection, feature engineering, and synchronization. At the
heart of our system are four distinct machine learning algorithms: Random
Forests predict and optimize water temperature and pH levels for the fish,
fostering their health and growth; Support Vector Machines (SVMs) function as
an early warning system, promptly detecting diseases and parasites in fish;
Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule
based on real-time environmental conditions, promoting resource efficiency and
fish productivity; Neural Networks manage the operation of critical equipment
like water pumps and heaters to maintain the desired environmental conditions
within the farm. These machine learning algorithms collaboratively make
real-time decisions to ensure that the fish farm's environmental conditions
align with predefined specifications, leading to improved fish health and
productivity while simultaneously reducing resource wastage, thereby
contributing to increased profitability and sustainability. This research
article showcases the power of data-driven decision support in fish farming,
promising to meet the growing demand for seafood while emphasizing
environmental responsibility and economic viability, thus revolutionizing the
future of fish farming.